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Добірка наукової літератури з теми "BREAKHIS DATASET"

Автор: Grafiati

Опубліковано: 26 жовтня 2023

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Статті в журналах з теми "BREAKHIS DATASET"

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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2

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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3

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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4

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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5

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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6

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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7

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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Анотація:

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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Анотація:

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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