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Journal articles on the topic 'BREAKHIS DATASET'

Author: Grafiati

Published: 26 October 2023

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1

Joshi, Shubhangi A., Anupkumar M. Bongale, P. Olof Olsson, Siddhaling Urolagin, Deepak Dharrao, and Arunkumar Bongale. "Enhanced Pre-Trained Xception Model Transfer Learned for Breast Cancer Detection." Computation 11, no. 3 (2023): 59. http://dx.doi.org/10.3390/computation11030059.

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Early detection and timely breast cancer treatment improve survival rates and patients’ quality of life. Hence, many computer-assisted techniques based on artificial intelligence are being introduced into the traditional diagnostic workflow. This inclusion of automatic diagnostic systems speeds up diagnosis and helps medical professionals by relieving their work pressure. This study proposes a breast cancer detection framework based on a deep convolutional neural network. To mine useful information about breast cancer through breast histopathology images of the 40× magnification factor that ar

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Xu, Xuebin, Meijuan An, Jiada Zhang, Wei Liu, and Longbin Lu. "A High-Precision Classification Method of Mammary Cancer Based on Improved DenseNet Driven by an Attention Mechanism." Computational and Mathematical Methods in Medicine 2022 (May 14, 2022): 1–14. http://dx.doi.org/10.1155/2022/8585036.

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Cancer is one of the major causes of human disease and death worldwide, and mammary cancer is one of the most common cancer types among women today. In this paper, we used the deep learning method to conduct a preliminary experiment on Breast Cancer Histopathological Database (BreakHis); BreakHis is an open dataset. We propose a high-precision classification method of mammary based on an improved convolutional neural network on the BreakHis dataset. We proposed three different MFSCNET models according to the different insertion positions and the number of SE modules, respectively, MFSCNet A, M

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Ogundokun, Roseline Oluwaseun, Sanjay Misra, Akinyemi Omololu Akinrotimi, and Hasan Ogul. "MobileNet-SVM: A Lightweight Deep Transfer Learning Model to Diagnose BCH Scans for IoMT-Based Imaging Sensors." Sensors 23, no. 2 (2023): 656. http://dx.doi.org/10.3390/s23020656.

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Many individuals worldwide pass away as a result of inadequate procedures for prompt illness identification and subsequent treatment. A valuable life can be saved or at least extended with the early identification of serious illnesses, such as various cancers and other life-threatening conditions. The development of the Internet of Medical Things (IoMT) has made it possible for healthcare technology to offer the general public efficient medical services and make a significant contribution to patients’ recoveries. By using IoMT to diagnose and examine BreakHis v1 400× breast cancer histology (B

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Ukwuoma, Chiagoziem C., Md Altab Hossain, Jehoiada K. Jackson, Grace U. Nneji, Happy N. Monday, and Zhiguang Qin. "Multi-Classification of Breast Cancer Lesions in Histopathological Images Using DEEP_Pachi: Multiple Self-Attention Head." Diagnostics 12, no. 5 (2022): 1152. http://dx.doi.org/10.3390/diagnostics12051152.

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Introduction and Background: Despite fast developments in the medical field, histological diagnosis is still regarded as the benchmark in cancer diagnosis. However, the input image feature extraction that is used to determine the severity of cancer at various magnifications is harrowing since manual procedures are biased, time consuming, labor intensive, and error-prone. Current state-of-the-art deep learning approaches for breast histopathology image classification take features from entire images (generic features). Thus, they are likely to overlook the essential image features for the unnec

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Mohanakurup, Vinodkumar, Syam Machinathu Parambil Gangadharan, Pallavi Goel, et al. "Breast Cancer Detection on Histopathological Images Using a Composite Dilated Backbone Network." Computational Intelligence and Neuroscience 2022 (July 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/8517706.

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Breast cancer is a lethal illness that has a high mortality rate. In treatment, the accuracy of diagnosis is crucial. Machine learning and deep learning may be beneficial to doctors. The proposed backbone network is critical for the present performance of CNN-based detectors. Integrating dilated convolution, ResNet, and Alexnet increases detection performance. The composite dilated backbone network (CDBN) is an innovative method for integrating many identical backbones into a single robust backbone. Hence, CDBN uses the lead backbone feature maps to identify objects. It feeds high-level output

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Nahid, Abdullah-Al, Mohamad Ali Mehrabi, and Yinan Kong. "Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering." BioMed Research International 2018 (2018): 1–20. http://dx.doi.org/10.1155/2018/2362108.

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Breast Cancer is a serious threat and one of the largest causes of death of women throughout the world. The identification of cancer largely depends on digital biomedical photography analysis such as histopathological images by doctors and physicians. Analyzing histopathological images is a nontrivial task, and decisions from investigation of these kinds of images always require specialised knowledge. However, Computer Aided Diagnosis (CAD) techniques can help the doctor make more reliable decisions. The state-of-the-art Deep Neural Network (DNN) has been recently introduced for biomedical ima

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Sun, Yixin, Lei Wu, Peng Chen, Feng Zhang, and Lifeng Xu. "Using deep learning in pathology image analysis: A novel active learning strategy based on latent representation." Electronic Research Archive 31, no. 9 (2023): 5340–61. http://dx.doi.org/10.3934/era.2023271.

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<abstract><p>Most countries worldwide continue to encounter a pathologist shortage, significantly impeding the timely diagnosis and effective treatment of cancer patients. Deep learning techniques have performed remarkably well in pathology image analysis; however, they require expert pathologists to annotate substantial pathology image data. This study aims to minimize the need for data annotation to analyze pathology images. Active learning (AL) is an iterative approach to search for a few high-quality samples to train a model. We propose our active learning framework, which firs

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Istighosah, Maie, Andi Sunyoto, and Tonny Hidayat. "Breast Cancer Detection in Histopathology Images using ResNet101 Architecture." sinkron 8, no. 4 (2023): 2138–49. http://dx.doi.org/10.33395/sinkron.v8i4.12948.

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Cancer is a significant challenge in many fields, especially health and medicine. Breast cancer is among the most common and frequent cancers in women worldwide. Early detection of cancer is the main step for early treatment and increasing the chances of patient survival. As the convolutional neural network method has grown in popularity, breast cancer can be easily identified without the help of experts. Using BreaKHis histopathology data, this project will assess the efficacy of the CNN architecture ResNet101 for breast cancer image classification. The dataset is divided into two classes, na

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Li, Lingxiao, Niantao Xie, and Sha Yuan. "A Federated Learning Framework for Breast Cancer Histopathological Image Classification." Electronics 11, no. 22 (2022): 3767. http://dx.doi.org/10.3390/electronics11223767.

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Quantities and diversities of datasets are vital to model training in a variety of medical image diagnosis applications. However, there are the following problems in real scenes: the required data may not be available in a single institution due to the number of patients or the type of pathology, and it is often not feasible to share patient data due to medical data privacy regulations. This means keeping private data safe is required and has become an obstacle in fusing data from multi-party to train a medical model. To solve the problems, we propose a federated learning framework, which allo

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Burrai, Giovanni P., Andrea Gabrieli, Marta Polinas, et al. "Canine Mammary Tumor Histopathological Image Classification via Computer-Aided Pathology: An Available Dataset for Imaging Analysis." Animals 13, no. 9 (2023): 1563. http://dx.doi.org/10.3390/ani13091563.

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Histopathology, the gold-standard technique in classifying canine mammary tumors (CMTs), is a time-consuming process, affected by high inter-observer variability. Digital (DP) and Computer-aided pathology (CAD) are emergent fields that will improve overall classification accuracy. In this study, the ability of the CAD systems to distinguish benign from malignant CMTs has been explored on a dataset—namely CMTD—of 1056 hematoxylin and eosin JPEG images from 20 benign and 24 malignant CMTs, with three different CAD systems based on the combination of a convolutional neural network (VGG16, Incepti

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Minarno, Agus Eko, Lulita Ria Wandani, and Yufis Azhar. "Classification of Breast Cancer Based on Histopathological Image Using EfficientNet-B0 on Convolutional Neural Network." International Journal of Emerging Technology and Advanced Engineering 12, no. 8 (2022): 70–77. http://dx.doi.org/10.46338/ijetae0822_09.

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Breast cancer has been chosen as the leading cause of cancer-related death in women. Biopsy is still the most accurate way to detect cancer cells. However, this is time-consuming and requires a relatively expensive cost and requires a pathologist. Advances in machine learning make it possible to detect and diagnose breast cancer using histopathological images that are the result of a biopsy. BreakHis dataset is a dataset that provides histopathological images. This study proposes the use of this dataset for cancer classification based on histopathological images using EfficientNet-B0 on the Co

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Agbley, Bless Lord Y., Jianping Li, Md Altab Hossin, et al. "Federated Learning-Based Detection of Invasive Carcinoma of No Special Type with Histopathological Images." Diagnostics 12, no. 7 (2022): 1669. http://dx.doi.org/10.3390/diagnostics12071669.

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Invasive carcinoma of no special type (IC-NST) is known to be one of the most prevalent kinds of breast cancer, hence the growing research interest in studying automated systems that can detect the presence of breast tumors and appropriately classify them into subtypes. Machine learning (ML) and, more specifically, deep learning (DL) techniques have been used to approach this problem. However, such techniques usually require massive amounts of data to obtain competitive results. This requirement makes their application in specific areas such as health problematic as privacy concerns regarding

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Mewada, Hiren K., Amit V. Patel, Mahmoud Hassaballah, Monagi H. Alkinani, and Keyur Mahant. "Spectral–Spatial Features Integrated Convolution Neural Network for Breast Cancer Classification." Sensors 20, no. 17 (2020): 4747. http://dx.doi.org/10.3390/s20174747.

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Cancer identification and classification from histopathological images of the breast depends greatly on experts, and computer-aided diagnosis can play an important role in disagreement of experts. This automatic process has increased the accuracy of the classification at a reduced cost. The advancement in Convolution Neural Network (CNN) structure has outperformed the traditional approaches in biomedical imaging applications. One of the limiting factors of CNN is it uses spatial image features only for classification. The spectral features from the transform domain have equivalent importance i

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Li, Xin, HongBo Li, WenSheng Cui, ZhaoHui Cai, and MeiJuan Jia. "Classification on Digital Pathological Images of Breast Cancer Based on Deep Features of Different Levels." Mathematical Problems in Engineering 2021 (December 30, 2021): 1–13. http://dx.doi.org/10.1155/2021/8403025.

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Breast cancer is one of the primary causes of cancer death in the world and has a great impact on women’s health. Generally, the majority of classification methods rely on the high-level feature. However, different levels of features may not be positively correlated for the final results of classification. Inspired by the recent widespread use of deep learning, this study proposes a novel method for classifying benign cancer and malignant breast cancer based on deep features. First, we design Sliding + Random and Sliding + Class Balance Random window slicing strategies for data preprocessing.

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Amato, Domenico, Salvatore Calderaro, Giosué Lo Bosco, Riccardo Rizzo, and Filippo Vella. "Metric Learning in Histopathological Image Classification: Opening the Black Box." Sensors 23, no. 13 (2023): 6003. http://dx.doi.org/10.3390/s23136003.

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The application of machine learning techniques to histopathology images enables advances in the field, providing valuable tools that can speed up and facilitate the diagnosis process. The classification of these images is a relevant aid for physicians who have to process a large number of images in long and repetitive tasks. This work proposes the adoption of metric learning that, beyond the task of classifying images, can provide additional information able to support the decision of the classification system. In particular, triplet networks have been employed to create a representation in th

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Liu, Min, Yu He, Minghu Wu, and Chunyan Zeng. "Breast Histopathological Image Classification Method Based on Autoencoder and Siamese Framework." Information 13, no. 3 (2022): 107. http://dx.doi.org/10.3390/info13030107.

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The automated classification of breast cancer histopathological images is one of the important tasks in computer-aided diagnosis systems (CADs). Due to the characteristics of small inter-class and large intra-class variances in breast cancer histopathological images, extracting features for breast cancer classification is difficult. To address this problem, an improved autoencoder (AE) network using a Siamese framework that can learn the effective features from histopathological images for CAD breast cancer classification tasks was designed. First, the inputted image is processed at multiple s

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Umer, Muhammad Junaid, Muhammad Sharif, Seifedine Kadry, and Abdullah Alharbi. "Multi-Class Classification of Breast Cancer Using 6B-Net with Deep Feature Fusion and Selection Method." Journal of Personalized Medicine 12, no. 5 (2022): 683. http://dx.doi.org/10.3390/jpm12050683.

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Breast cancer has now overtaken lung cancer as the world’s most commonly diagnosed cancer, with thousands of new cases per year. Early detection and classification of breast cancer are necessary to overcome the death rate. Recently, many deep learning-based studies have been proposed for automatic diagnosis and classification of this deadly disease, using histopathology images. This study proposed a novel solution for multi-class breast cancer classification from histopathology images using deep learning. For this purpose, a novel 6B-Net deep CNN model, with feature fusion and selection mechan

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Umer, Muhammad Junaid, Muhammad Sharif, Seifedine Kadry, and Abdullah Alharbi. "Multi-Class Classification of Breast Cancer Using 6B-Net with Deep Feature Fusion and Selection Method." Journal of Personalized Medicine 12, no. 5 (2022): 683. http://dx.doi.org/10.3390/jpm12050683.

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Breast cancer has now overtaken lung cancer as the world’s most commonly diagnosed cancer, with thousands of new cases per year. Early detection and classification of breast cancer are necessary to overcome the death rate. Recently, many deep learning-based studies have been proposed for automatic diagnosis and classification of this deadly disease, using histopathology images. This study proposed a novel solution for multi-class breast cancer classification from histopathology images using deep learning. For this purpose, a novel 6B-Net deep CNN model, with feature fusion and selection mechan

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Sarker, Md Mostafa Kamal, Farhan Akram, Mohammad Alsharid, Vivek Kumar Singh, Robail Yasrab, and Eyad Elyan. "Efficient Breast Cancer Classification Network with Dual Squeeze and Excitation in Histopathological Images." Diagnostics 13, no. 1 (2022): 103. http://dx.doi.org/10.3390/diagnostics13010103.

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Medical image analysis methods for mammograms, ultrasound, and magnetic resonance imaging (MRI) cannot provide the underline features on the cellular level to understand the cancer microenvironment which makes them unsuitable for breast cancer subtype classification study. In this paper, we propose a convolutional neural network (CNN)-based breast cancer classification method for hematoxylin and eosin (H&E) whole slide images (WSIs). The proposed method incorporates fused mobile inverted bottleneck convolutions (FMB-Conv) and mobile inverted bottleneck convolutions (MBConv) with a dual squ

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Chandranegara, Didih Rizki, Faras Haidar Pratama, Sidiq Fajrianur, Moch Rizky Eka Putra, and Zamah Sari. "Automated Detection of Breast Cancer Histopathology Image Using Convolutional Neural Network and Transfer Learning." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 22, no. 3 (2023): 455–68. http://dx.doi.org/10.30812/matrik.v22i3.2803.

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cancer caused 2.3 million cases and 685,000 deaths in 2020. Histopathology analysis is one of the tests used to determine a patient’s prognosis. However, histopathology analysis is a time-consuming and stressful process. With advances in deep learning methods, computer vision science can be used to detect cancer in medical images, which is expected to improve the accuracy of prognosis. This study aimed to apply Convolutional Neural Network (CNN) and Transfer Learning methods to classify breast cancer histopathology images to diagnose breast tumors. This method used CNN, Transfer Learning ((Vis

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Wakili, Musa Adamu, Harisu Abdullahi Shehu, Md Haidar Sharif, et al. "Classification of Breast Cancer Histopathological Images Using DenseNet and Transfer Learning." Computational Intelligence and Neuroscience 2022 (October 10, 2022): 1–31. http://dx.doi.org/10.1155/2022/8904768.

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Breast cancer is one of the most common invading cancers in women. Analyzing breast cancer is nontrivial and may lead to disagreements among experts. Although deep learning methods achieved an excellent performance in classification tasks including breast cancer histopathological images, the existing state-of-the-art methods are computationally expensive and may overfit due to extracting features from in-distribution images. In this paper, our contribution is mainly twofold. First, we perform a short survey on deep-learning-based models for classifying histopathological images to investigate t

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Alirezazadeh, Pendar, Fadi Dornaika, and Abdelmalik Moujahid. "Chasing a Better Decision Margin for Discriminative Histopathological Breast Cancer Image Classification." Electronics 12, no. 20 (2023): 4356. http://dx.doi.org/10.3390/electronics12204356.

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When considering a large dataset of histopathologic breast images captured at various magnification levels, the process of distinguishing between benign and malignant cancer from these images can be time-intensive. The automation of histopathological breast cancer image classification holds significant promise for expediting pathology diagnoses and reducing the analysis time. Convolutional neural networks (CNNs) have recently gained traction for their ability to more accurately classify histopathological breast cancer images. CNNs excel at extracting distinctive features that emphasize semanti

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Zaalouk, Ahmed M., Gamal A. Ebrahim, Hoda K. Mohamed, Hoda Mamdouh Hassan, and Mohamed M. A. Zaalouk. "A Deep Learning Computer-Aided Diagnosis Approach for Breast Cancer." Bioengineering 9, no. 8 (2022): 391. http://dx.doi.org/10.3390/bioengineering9080391.

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Breast cancer is a gigantic burden on humanity, causing the loss of enormous numbers of lives and amounts of money. It is the world’s leading type of cancer among women and a leading cause of mortality and morbidity. The histopathological examination of breast tissue biopsies is the gold standard for diagnosis. In this paper, a computer-aided diagnosis (CAD) system based on deep learning is developed to ease the pathologist’s mission. For this target, five pre-trained convolutional neural network (CNN) models are analyzed and tested—Xception, DenseNet201, InceptionResNetV2, VGG19, and ResNet15

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Li, Jia, Jingwen Shi, Hexing Su, and Le Gao. "Breast Cancer Histopathological Image Recognition Based on Pyramid Gray Level Co-Occurrence Matrix and Incremental Broad Learning." Electronics 11, no. 15 (2022): 2322. http://dx.doi.org/10.3390/electronics11152322.

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In order to recognize breast cancer histopathological images, this article proposed a combined model consisting of a pyramid gray level co-occurrence matrix (PGLCM) feature extraction model and an incremental broad learning (IBL) classification model. The PGLCM model is designed to extract the fusion features of breast cancer histopathological images, which can reflect the multiresolution useful information of the images and facilitate the improvement of the classification effect in the later stage. The IBL model is used to improve the classification accuracy by increasing the number of networ

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Jae Lim, Myung, Da Eun Kim, Dong Kun Chung, Hoon Lim, and Young Man Kwon. "Deep Convolution Neural Networks for Medical Image Analysis." International Journal of Engineering & Technology 7, no. 3.33 (2018): 115. http://dx.doi.org/10.14419/ijet.v7i3.33.18588.

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Breast cancer is a highly contagious disease that has killed many people all over the world. It can be fully recovered from early detection. To enable the early detection of the breast cancer, it is very important to classify accurately whether it is breast cancer or not. Recently, the deep learning approach method on the medical images such as these histopathologic images of the breast cancer is showing higher level of accuracy and efficiency compared to the conventional methods. In this paper, the breast cancer histopathological image that is difficult to be distinguished was analyzed visual

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Kode, Hepseeba, and Buket D. Barkana. "Deep Learning- and Expert Knowledge-Based Feature Extraction and Performance Evaluation in Breast Histopathology Images." Cancers 15, no. 12 (2023): 3075. http://dx.doi.org/10.3390/cancers15123075.

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Cancer develops when a single or a group of cells grows and spreads uncontrollably. Histopathology images are used in cancer diagnosis since they show tissue and cell structures under a microscope. Knowledge-based and deep learning-based computer-aided detection is an ongoing research field in cancer diagnosis using histopathology images. Feature extraction is vital in both approaches since the feature set is fed to a classifier and determines the performance. This paper evaluates three feature extraction methods and their performance in breast cancer diagnosis. Features are extracted by (1) a

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Leow, Jia Rong, Wee How Khoh, Ying Han Pang, and Hui Yen Yap. "Breast cancer classification with histopathological image based on machine learning." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 5 (2023): 5885. http://dx.doi.org/10.11591/ijece.v13i5.pp5885-5897.

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<span lang="EN-US">Breast cancer represents one of the most common reasons for death in the worldwide. It has a substantially higher death rate than other types of cancer. Early detection can enhance the chances of receiving proper treatment and survival. In order to address this problem, this work has provided a convolutional neural network (CNN) deep learning (DL) based model on the classification that may be used to differentiate breast cancer histopathology images as benign or malignant. Besides that, five different types of pre-trained CNN architectures have been used to investigate

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Tummala, Sudhakar, Jungeun Kim, and Seifedine Kadry. "BreaST-Net: Multi-Class Classification of Breast Cancer from Histopathological Images Using Ensemble of Swin Transformers." Mathematics 10, no. 21 (2022): 4109. http://dx.doi.org/10.3390/math10214109.

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Breast cancer (BC) is one of the deadly forms of cancer, causing mortality worldwide in the female population. The standard imaging procedures for screening BC involve mammography and ultrasonography. However, these imaging procedures cannot differentiate subtypes of benign and malignant cancers. Here, histopathology images could provide better sensitivity toward benign and malignant cancer subtypes. Recently, vision transformers have been gaining attention in medical imaging due to their success in various computer vision tasks. Swin transformer (SwinT) is a variant of vision transformer that

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Kaplun, Dmitry, Alexander Krasichkov, Petr Chetyrbok, Nikolay Oleinikov, Anupam Garg, and Husanbir Singh Pannu. "Cancer Cell Profiling Using Image Moments and Neural Networks with Model Agnostic Explainability: A Case Study of Breast Cancer Histopathological (BreakHis) Database." Mathematics 9, no. 20 (2021): 2616. http://dx.doi.org/10.3390/math9202616.

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With the evolution of modern digital pathology, examining cancer cell tissues has paved the way to quantify subtle symptoms, for example, by means of image staining procedures using Eosin and Hematoxylin. Cancer tissues in the case of breast and lung cancer are quite challenging to examine by manual expert analysis of patients suffering from cancer. Merely relying on the observable characteristics by histopathologists for cell profiling may under-constrain the scale and diagnostic quality due to tedious repetition with constant concentration. Thus, automatic analysis of cancer cells has been p

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Chopra, Pooja, N. Junath, Sitesh Kumar Singh, Shakir Khan, R. Sugumar, and Mithun Bhowmick. "Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task." BioMed Research International 2022 (July 21, 2022): 1–12. http://dx.doi.org/10.1155/2022/6336700.

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An algorithm framework based on CycleGAN and an upgraded dual-path network (DPN) is suggested to address the difficulties of uneven staining in pathological pictures and difficulty of discriminating benign from malignant cells. CycleGAN is used for color normalization in pathological pictures to tackle the problem of uneven staining. However, the resultant detection model is ineffective. By overlapping the images, the DPN uses the addition of small convolution, deconvolution, and attention mechanisms to enhance the model’s ability to classify the texture features of pathological images on the

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Elshafey, Mohamed Abdelmoneim, and Tarek Elsaid Ghoniemy. "A hybrid ensemble deep learning approach for reliable breast cancer detection." International Journal of Advances in Intelligent Informatics 7, no. 2 (2021): 112. http://dx.doi.org/10.26555/ijain.v7i2.615.

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Among the cancer diseases, breast cancer is considered one of the most prevalent threats requiring early detection for a higher recovery rate. Meanwhile, the manual evaluation of malignant tissue regions in histopathology images is a critical and challenging task. Nowadays, deep learning becomes a leading technology for automatic tumor feature extraction and classification as malignant or benign. This paper presents a proposed hybrid deep learning-based approach, for reliable breast cancer detection, in three consecutive stages: 1) fine-tuning the pre-trained Xception-based classification mode

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Yang, Yunfeng, and Chen Guan. "Classification of histopathological images of breast cancer using an improved convolutional neural network model." Journal of X-Ray Science and Technology 30, no. 1 (2022): 33–44. http://dx.doi.org/10.3233/xst-210982.

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The accurately automatic classification of medical pathological images has always been an important problem in the field of deep learning. However, the traditional manual extraction of features and image classification usually requires in-depth knowledge and more professional researchers to extract and calculate high-quality image features. This kind of operation generally takes a lot of time and the classification effect is not ideal. In order to solve these problems, this study proposes and tests an improved network model DenseNet-201-MSD to accomplish the task of classification of medical p

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Saha, Priya, Puja Das, Niharika Nath, and Mrinal Kanti Bhowmik. "Estimation of Abnormal Cell Growth and MCG-Based Discriminative Feature Analysis of Histopathological Breast Images." International Journal of Intelligent Systems 2023 (June 30, 2023): 1–12. http://dx.doi.org/10.1155/2023/6318127.

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The accurate prediction of cancer from microscopic biopsy images has always been a major challenge for medical practitioners and pathologists who manually observe the shape and structure of the cells from tissues under a microscope. Mathematical modelling of cell proliferation helps to predict tumour sizes and optimizes the treatment procedure. This paper introduces a cell growth estimation function that uncovers the growth behaviour of benign and malignant cells. To analyse the cellular level information from tissue images, we propose a minimized cellular graph (MCG) development method. The m

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Hao, Yan, Li Zhang, Shichang Qiao, et al. "Breast cancer histopathological images classification based on deep semantic features and gray level co-occurrence matrix." PLOS ONE 17, no. 5 (2022): e0267955. http://dx.doi.org/10.1371/journal.pone.0267955.

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Breast cancer is regarded as the leading killer of women today. The early diagnosis and treatment of breast cancer is the key to improving the survival rate of patients. A method of breast cancer histopathological images recognition based on deep semantic features and gray level co-occurrence matrix (GLCM) features is proposed in this paper. Taking the pre-trained DenseNet201 as the basic model, part of the convolutional layer features of the last dense block are extracted as the deep semantic features, which are then fused with the three-channel GLCM features, and the support vector machine (

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Lee, Jiann-Shu, and Wen-Kai Wu. "Breast Tumor Tissue Image Classification Using DIU-Net." Sensors 22, no. 24 (2022): 9838. http://dx.doi.org/10.3390/s22249838.

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Inspired by the observation that pathologists pay more attention to the nuclei regions when analyzing pathological images, this study utilized soft segmentation to imitate the visual focus mechanism and proposed a new segmentation–classification joint model to achieve superior classification performance for breast cancer pathology images. Aiming at the characteristics of different sizes of nuclei in pathological images, this study developed a new segmentation network with excellent cross-scale description ability called DIU-Net. To enhance the generalization ability of the segmentation network

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Ashurov, Asadulla, Samia Allaoua Chelloug, Alexey Tselykh, Mohammed Saleh Ali Muthanna, Ammar Muthanna, and Mehdhar S. A. M. Al-Gaashani. "Improved Breast Cancer Classification through Combining Transfer Learning and Attention Mechanism." Life 13, no. 9 (2023): 1945. http://dx.doi.org/10.3390/life13091945.

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Breast cancer, a leading cause of female mortality worldwide, poses a significant health challenge. Recent advancements in deep learning techniques have revolutionized breast cancer pathology by enabling accurate image classification. Various imaging methods, such as mammography, CT, MRI, ultrasound, and biopsies, aid in breast cancer detection. Computer-assisted pathological image classification is of paramount importance for breast cancer diagnosis. This study introduces a novel approach to breast cancer histopathological image classification. It leverages modified pre-trained CNN models and

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Alqahtani, Yahya, Umakant Mandawkar, Aditi Sharma, Mohammad Najmus Saquib Hasan, Mrunalini Harish Kulkarni, and R. Sugumar. "Breast Cancer Pathological Image Classification Based on the Multiscale CNN Squeeze Model." Computational Intelligence and Neuroscience 2022 (August 29, 2022): 1–11. http://dx.doi.org/10.1155/2022/7075408.

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The use of an automatic histopathological image identification system is essential for expediting diagnoses and lowering mistake rates. Although it is of enormous clinical importance, computerized breast cancer multiclassification using histological pictures has rarely been investigated. A deep learning-based classification strategy is suggested to solve the challenge of automated categorization of breast cancer pathology pictures. The attention model that acts on the feature channel is the channel refinement model. The learned channel weight may be used to reduce superfluous features when imp

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Burçak, Kadir Can, and Harun Uğuz. "A New Hybrid Breast Cancer Diagnosis Model Using Deep Learning Model and ReliefF." Traitement du Signal 39, no. 2 (2022): 521–29. http://dx.doi.org/10.18280/ts.390214.

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Breast cancer is a dangerous type of cancer usually found in women and is a significant research topic in medical science. In patients who are diagnosed and not treated early, cancer spreads to other organs, making treatment difficult. In breast cancer diagnosis, the accuracy of the pathological diagnosis is of great importance to shorten the decision-making process, minimize unnoticed cancer cells and obtain a faster diagnosis. However, the similarity of images in histopathological breast cancer image analysis is a sensitive and difficult process that requires high competence for field expert

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Asare, Sarpong Kwadwo, Fei You, and Obed Tettey Nartey. "A Semisupervised Learning Scheme with Self-Paced Learning for Classifying Breast Cancer Histopathological Images." Computational Intelligence and Neuroscience 2020 (December 8, 2020): 1–16. http://dx.doi.org/10.1155/2020/8826568.

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The unavailability of large amounts of well-labeled data poses a significant challenge in many medical imaging tasks. Even in the likelihood of having access to sufficient data, the process of accurately labeling the data is an arduous and time-consuming one, requiring expertise skills. Again, the issue of unbalanced data further compounds the abovementioned problems and presents a considerable challenge for many machine learning algorithms. In lieu of this, the ability to develop algorithms that can exploit large amounts of unlabeled data together with a small amount of labeled data, while de

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Tangsakul, Surasak, and Sartra Wongthanavasu. "Deep Cellular Automata-Based Feature Extraction for Classification of the Breast Cancer Image." Applied Sciences 13, no. 10 (2023): 6081. http://dx.doi.org/10.3390/app13106081.

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Feature extraction is an important step in classification. It directly results in an improvement of classification performance. Recent successes of convolutional neural networks (CNN) have revolutionized image classification in computer vision. The outstanding convolution layer of CNN performs feature extraction to obtain promising features from images. However, it faces the overfitting problem and computational complexity due to the complicated structure of the convolution layer and deep computation. Therefore, this research problem is challenging. This paper proposes a novel deep feature ext

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Boumaraf, Said, Xiabi Liu, Yuchai Wan, et al. "Conventional Machine Learning versus Deep Learning for Magnification Dependent Histopathological Breast Cancer Image Classification: A Comparative Study with Visual Explanation." Diagnostics 11, no. 3 (2021): 528. http://dx.doi.org/10.3390/diagnostics11030528.

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Breast cancer is a serious threat to women. Many machine learning-based computer-aided diagnosis (CAD) methods have been proposed for the early diagnosis of breast cancer based on histopathological images. Even though many such classification methods achieved high accuracy, many of them lack the explanation of the classification process. In this paper, we compare the performance of conventional machine learning (CML) against deep learning (DL)-based methods. We also provide a visual interpretation for the task of classifying breast cancer in histopathological images. For CML-based methods, we

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Wang, Jiatong, Tiantian Zhu, Shan Liang, R. Karthiga, K. Narasimhan, and V. Elamaran. "Binary and Multiclass Classification of Histopathological Images Using Machine Learning Techniques." Journal of Medical Imaging and Health Informatics 10, no. 9 (2020): 2252–58. http://dx.doi.org/10.1166/jmihi.2020.3124.

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Background and Objective: Breast cancer is fairly common and widespread form of cancer among women. Digital mammogram, thermal images of breast and digital histopathological images serve as a major tool for the diagnosis and grading of cancer. In this paper, a novel attempt has been proposed using image analysis and machine learning algorithm to develop an automated system for the diagnosis and grading of cancer. Methods: BreaKHis dataset is employed for the present work where images are available with different magnification factor namely 40×, 100×, 200×, 400× and 200× magnification factor is

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Jakkaladiki, Sudha Prathyusha, and Filip Maly. "An efficient transfer learning based cross model classification (TLBCM) technique for the prediction of breast cancer." PeerJ Computer Science 9 (March 21, 2023): e1281. http://dx.doi.org/10.7717/peerj-cs.1281.

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Breast cancer has been the most life-threatening disease in women in the last few decades. The high mortality rate among women is due to breast cancer because of less awareness and a minimum number of medical facilities to detect the disease in the early stages. In the recent era, the situation has changed with the help of many technological advancements and medical equipment to observe breast cancer development. The machine learning technique supports vector machines (SVM), logistic regression, and random forests have been used to analyze the images of cancer cells on different data sets. Alt

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Clement, David, Emmanuel Agu, Muhammad A. Suleiman, John Obayemi, Steve Adeshina, and Wole Soboyejo. "Multi-Class Breast Cancer Histopathological Image Classification Using Multi-Scale Pooled Image Feature Representation (MPIFR) and One-Versus-One Support Vector Machines." Applied Sciences 13, no. 1 (2022): 156. http://dx.doi.org/10.3390/app13010156.

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Breast cancer (BC) is currently the most common form of cancer diagnosed worldwide with an incidence estimated at 2.26 million in 2020. Additionally, BC is the leading cause of cancer death. Many subtypes of breast cancer exist with distinct biological features and which respond differently to various treatment modalities and have different clinical outcomes. To ensure that sufferers receive lifesaving patients-tailored treatment early, it is crucial to accurately distinguish dangerous malignant (ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma) subtypes of tumo

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Clement, David, Emmanuel Agu, John Obayemi, Steve Adeshina, and Wole Soboyejo. "Breast Cancer Tumor Classification Using a Bag of Deep Multi-Resolution Convolutional Features." Informatics 9, no. 4 (2022): 91. http://dx.doi.org/10.3390/informatics9040091.

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Breast cancer accounts for 30% of all female cancers. Accurately distinguishing dangerous malignant tumors from benign harmless ones is key to ensuring patients receive lifesaving treatments on time. However, as doctors currently do not identify 10% to 30% of breast cancers during regular assessment, automated methods to detect malignant tumors are desirable. Although several computerized methods for breast cancer classification have been proposed, convolutional neural networks (CNNs) have demonstrably outperformed other approaches. In this paper, we propose an automated method for the binary

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Lu, Shida, Kai Huang, Talha Meraj, and Hafiz Tayyab Rauf. "A novel CAPTCHA solver framework using deep skipping Convolutional Neural Networks." PeerJ Computer Science 8 (April 6, 2022): e879. http://dx.doi.org/10.7717/peerj-cs.879.

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A Completely Automated Public Turing Test to tell Computers and Humans Apart (CAPTCHA) is used in web systems to secure authentication purposes; it may break using Optical Character Recognition (OCR) type methods. CAPTCHA breakers make web systems highly insecure. However, several techniques to break CAPTCHA suggest CAPTCHA designers about their designed CAPTCHA’s need improvement to prevent computer vision-based malicious attacks. This research primarily used deep learning methods to break state-of-the-art CAPTCHA codes; however, the validation scheme and conventional Convolutional Neural Net

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Tao, Ran, Zhaoya Gong, Qiwei Ma, and Jean-Claude Thill. "Boosting Computational Effectiveness in Big Spatial Flow Data Analysis with Intelligent Data Reduction." ISPRS International Journal of Geo-Information 9, no. 5 (2020): 299. http://dx.doi.org/10.3390/ijgi9050299.

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One of the enduring issues of spatial origin-destination (OD) flow data analysis is the computational inefficiency or even the impossibility to handle large datasets. Despite the recent advancements in high performance computing (HPC) and the ready availability of powerful computing infrastructure, we argue that the best solutions are based on a thorough understanding of the fundamental properties of the data. This paper focuses on overcoming the computational challenge through data reduction that intelligently takes advantage of the heavy-tailed distributional property of most flow datasets.

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Tang, Yansong, Xingyu Liu, Xumin Yu, Danyang Zhang, Jiwen Lu, and Jie Zhou. "Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 2 (2022): 1–24. http://dx.doi.org/10.1145/3472722.

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Rapid progress and superior performance have been achieved for skeleton-based action recognition recently. In this article, we investigate this problem under a cross-dataset setting, which is a new, pragmatic, and challenging task in real-world scenarios. Following the unsupervised domain adaptation (UDA) paradigm, the action labels are only available on a source dataset, but unavailable on a target dataset in the training stage. Different from the conventional adversarial learning-based approaches for UDA, we utilize a self-supervision scheme to reduce the domain shift between two skeleton-ba

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Isthigosah, Maie, Andi Sunyoto, and Tonny Hidayat. "Image Augmentation for BreaKHis Medical Data using Convolutional Neural Networks." sinkron 8, no. 4 (2023): 2381–92. http://dx.doi.org/10.33395/sinkron.v8i4.12878.

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In applying Convolutional Neural Network (CNN) to computer vision tasks in the medical domain, it is necessary to have sufficient datasets to train models with high accuracy and good general ability in identifying important patterns in medical data. This overfitting is exacerbated by data imbalances, where some classes may have a smaller sample size than others, leading to biased predictive results. The purpose of this augmentation is to create variation in the training data, which in turn can help reduce overfitting and increase the ability of the model to generalize. Therefore, comparing aug

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Laporte, Matias, Martin Gjoreski, and Marc Langheinrich. "LAUREATE." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, no. 3 (2023): 1–41. http://dx.doi.org/10.1145/3610892.

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The latest developments in wearable sensors have resulted in a wide range of devices available to consumers, allowing users to monitor and improve their physical activity, sleep patterns, cognitive load, and stress levels. However, the lack of out-of-the-lab labelled data hinders the development of advanced machine learning models for predicting affective states. Furthermore, to the best of our knowledge, there are no publicly available datasets in the area of Human Memory Augmentation. This paper presents a dataset we collected during a 13-week study in a university setting. The dataset, name

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