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Journal articles on the topic 'CBIS-DDSM'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Jasim, Hayder Nsaif, Wesam Mohammed Jasim Abid Alrawi, and Mohammed Salah Ibrahim Jassem. "Hyperparameter Optimisation for Breast Cancer Detection Using APO and Pre-Trained CNNs." International Journal of Online and Biomedical Engineering (iJOE) 21, no. 09 (2025): 96–109. https://doi.org/10.3991/ijoe.v21i09.55479.

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Early detection of breast cancer improves survival rates and treatment outcomes. Mammography remains the key diagnostic technique; however, building deep learning models for reliable categorisation is challenging. This paper presents a groundbreaking method for fine-tuning hyperparameters in two cutting-edge convolutional neural networks (CNNs), ConvNeXtBase and ResNet-50, which employ the Arctic Puffin Optimisation (APO) algorithm. Experiments were performed on two benchmark mammography datasets: CBIS-DDSM and MIAS. The APO-optimised ConvNeXtBase model achieved 98.46% accuracy on the CBIS-DDS

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TİRYAKİ, Volkan Müjdat. "Deep Transfer Learning to Classify Mass and Calcification Pathologies from Screen Film Mammograms." Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 12, no. 1 (2023): 57–65. http://dx.doi.org/10.17798/bitlisfen.1190134.

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The number of breast cancer diagnosis is the biggest among all cancers, but it can be treated if diagnosed early. Mammography is commonly used for detecting abnormalities and diagnosing the breast cancer. Breast cancer screening and diagnosis are still being performed by radiologists. In the last decade, deep learning was successfully applied on big image classification databases such as ImageNet. Deep learning methods for the automated breast cancer diagnosis is under investigation. In this study, breast cancer mass and calcification pathologies are classified by using deep transfer learning

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Alkhaleefah, Mohammad, Tan-Hsu Tan, Chuan-Hsun Chang, et al. "Connected-SegNets: A Deep Learning Model for Breast Tumor Segmentation from X-ray Images." Cancers 14, no. 16 (2022): 4030. http://dx.doi.org/10.3390/cancers14164030.

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Inspired by Connected-UNets, this study proposes a deep learning model, called Connected-SegNets, for breast tumor segmentation from X-ray images. In the proposed model, two SegNet architectures are connected with skip connections between their layers. Moreover, the cross-entropy loss function of the original SegNet has been replaced by the intersection over union (IoU) loss function in order to make the proposed model more robust against noise during the training process. As part of data preprocessing, a histogram equalization technique, called contrast limit adapt histogram equalization (CLA

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Thirumalaisamy, Selvakumar, Kamaleshwar Thangavilou, Hariharan Rajadurai, et al. "Breast Cancer Classification Using Synthesized Deep Learning Model with Metaheuristic Optimization Algorithm." Diagnostics 13, no. 18 (2023): 2925. http://dx.doi.org/10.3390/diagnostics13182925.

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Breast cancer is the second leading cause of mortality among women. Early and accurate detection plays a crucial role in lowering its mortality rate. Timely detection and classification of breast cancer enable the most effective treatment. Convolutional neural networks (CNNs) have significantly improved the accuracy of tumor detection and classification in medical imaging compared to traditional methods. This study proposes a comprehensive classification technique for identifying breast cancer, utilizing a synthesized CNN, an enhanced optimization algorithm, and transfer learning. The primary

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Ragab, Dina A., Maha Sharkas, Stephen Marshall, and Jinchang Ren. "Breast cancer detection using deep convolutional neural networks and support vector machines." PeerJ 7 (January 28, 2019): e6201. http://dx.doi.org/10.7717/peerj.6201.

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It is important to detect breast cancer as early as possible. In this manuscript, a new methodology for classifying breast cancer using deep learning and some segmentation techniques are introduced. A new computer aided detection (CAD) system is proposed for classifying benign and malignant mass tumors in breast mammography images. In this CAD system, two segmentation approaches are used. The first approach involves determining the region of interest (ROI) manually, while the second approach uses the technique of threshold and region based. The deep convolutional neural network (DCNN) is used

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Zhang, Qian, Yamei Li, Guohua Zhao, Panpan Man, Yusong Lin, and Meiyun Wang. "A Novel Algorithm for Breast Mass Classification in Digital Mammography Based on Feature Fusion." Journal of Healthcare Engineering 2020 (December 22, 2020): 1–11. http://dx.doi.org/10.1155/2020/8860011.

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Prompt diagnosis of benign and malignant breast masses is essential for early breast cancer screening. Convolutional neural networks (CNNs) can be used to assist in the classification of benign and malignant breast masses. A persistent problem in current mammography mass classification via CNN is the lack of local-invariant features, which cannot effectively respond to geometric image transformations or changes caused by imaging angles. In this study, a novel model that trains both texton representation and deep CNN representation for mass classification tasks is proposed. Rotation-invariant f

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Aliniya, Parvaneh, Mircea Nicolescu, Monica Nicolescu, and George Bebis. "Towards Robust Supervised Pectoral Muscle Segmentation in Mammography Images." Journal of Imaging 10, no. 12 (2024): 331. https://doi.org/10.3390/jimaging10120331.

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Mammography images are the most commonly used tool for breast cancer screening. The presence of pectoral muscle in images for the mediolateral oblique view makes designing a robust automated breast cancer detection system more challenging. Most of the current methods for removing the pectoral muscle are based on traditional machine learning approaches. This is partly due to the lack of segmentation masks of pectoral muscle in available datasets. In this paper, we provide the segmentation masks of the pectoral muscle for the INbreast, MIAS, and CBIS-DDSM datasets, which will enable the developm

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Mohammed, Ahmed Dhahi, and Dursun Ekmekci. "Breast Cancer Diagnosis Using YOLO-Based Multiscale Parallel CNN and Flattened Threshold Swish." Applied Sciences 14, no. 7 (2024): 2680. http://dx.doi.org/10.3390/app14072680.

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In the field of biomedical imaging, the use of Convolutional Neural Networks (CNNs) has achieved impressive success. Additionally, the detection and pathological classification of breast masses creates significant challenges. Traditional mammogram screening, conducted by healthcare professionals, is often exhausting, costly, and prone to errors. To address these issues, this research proposes an end-to-end Computer-Aided Diagnosis (CAD) system utilizing the ‘You Only Look Once’ (YOLO) architecture. The proposed framework begins by enhancing digital mammograms using the Contrast Limited Adaptiv

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Barthwal, Archana, Kapil Joshi, Adarsh Kumar, et al. "Blockchain and Classification of Mammograms and Histopathology Images in Breast Cancer Lesions." Applied and Computational Engineering 8, no. 1 (2023): 436–42. http://dx.doi.org/10.54254/2755-2721/8/20230208.

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This work provides an overview of the research that has been done on using Computer-Aided Detection (CAD) for the diagnosis of breast cancer. The focus is on mammographic and histopathology images and the different techniques used for image pre-processing, segmentation, and classification. The accuracy of the different algorithms was evaluated on different datasets, including MIAS, IRMA, DDSM, and CBIS-DDSM, and the results showed that deep learning models such as Convolutional Neural Networks (CNNs) and Random Forest, along with Multi-Layer Perception and Nave Bayes, were effective in detecti

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Khourdifi, Youness, Alae El Alami, Mounia Zaydi, Yassine Maleh, and Omar Er-Remyly. "Early Breast Cancer Detection Based on Deep Learning: An Ensemble Approach Applied to Mammograms." BioMedInformatics 4, no. 4 (2024): 2338–73. https://doi.org/10.3390/biomedinformatics4040127.

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Background: Breast cancer is one of the leading causes of death in women, making early detection through mammography crucial for improving survival rates. However, human interpretation of mammograms is often prone to diagnostic errors. This study addresses the challenge of improving the accuracy of breast cancer detection by leveraging advanced machine learning techniques. Methods: We propose an extended ensemble deep learning model that integrates three state-of-the-art convolutional neural network (CNN) architectures: VGG16, DenseNet121, and InceptionV3. The model utilizes multi-scale featur

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Falconi, Lenin G., Maria Perez, Wilbert G. Aguila, and Aura Conci. "Transfer Learning and Fine Tuning in Breast Mammogram Abnormalities Classification on CBIS-DDSM Database." Advances in Science, Technology and Engineering Systems Journal 5, no. 2 (2020): 154–65. http://dx.doi.org/10.25046/aj050220.

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Celis Esteban, Sergio Augusto, Jhoan Felipe Sarmiento Ortiz, and Liliana Calderón-Benavides. "DESARROLLO DE UNA RED NEURONAL CONVOLUCIONAL PARA LA DETECCIÓN DEL CÁNCER DE MAMA MEDIANTE LA CLASIFICACIÓN DE IMÁGENES MAMOGRÁFICAS." REVISTA COLOMBIANA DE TECNOLOGIAS DE AVANZADA (RCTA) 1, no. 39 (2023): 75–80. http://dx.doi.org/10.24054/rcta.v1i39.1378.

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La inteligencia artificial (IA) ha venido creciendo durante los últimos años en el área de la salud con el desarrollo de sistemas de apoyo a la toma de decisiones clínicas. Con este trabajo se logró desarrollar un algoritmo de aprendizaje profundo capaz de clasificar imágenes mamográficas en cinco categorías (normal, microcalcificación benigna, nódulo benigno, microcalcificación maligna y nódulo maligno) con un enfoque prioritario en la detección temprana del cáncer de mama, aplicando la técnica de transferencia de aprendizaje. Se usaron los conjuntos de datos DDSM y CBIS-DDSM, disponibles en

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Zahoor, Saliha, Umar Shoaib, and Ikram Ullah Lali. "Breast Cancer Mammograms Classification Using Deep Neural Network and Entropy-Controlled Whale Optimization Algorithm." Diagnostics 12, no. 2 (2022): 557. http://dx.doi.org/10.3390/diagnostics12020557.

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Breast cancer has affected many women worldwide. To perform detection and classification of breast cancer many computer-aided diagnosis (CAD) systems have been established because the inspection of the mammogram images by the radiologist is a difficult and time taken task. To early diagnose the disease and provide better treatment lot of CAD systems were established. There is still a need to improve existing CAD systems by incorporating new methods and technologies in order to provide more precise results. This paper aims to investigate ways to prevent the disease as well as to provide new met

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Habeeb, Zeyad Q., Branislav Vuksanovic, and Imad Q. Al-Zaydi. "Breast Cancer Detection Using Image Processing and Machine Learning." Journal of Image and Graphics 11, no. 1 (2023): 1–8. http://dx.doi.org/10.18178/joig.11.1.1-8.

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Different breast cancer detection systems have been developed to help clinicians analyze screening mammograms. Breast cancer has been increasing gradually so scientists work to develop new methods to reduce the risks of this life-threatening disease. Convolutional Neural Networks (CNNs) have shown much promise In the field of medical imaging because of recent developments in deep learning. However, CNN’s based methods have been restricted due to the small size of the few public breast cancer datasets. This research has developed a new framework and introduced it to detect breast cancer. This f

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Sait, Abdul Rahaman Wahab, and Ramprasad Nagaraj. "An Enhanced LightGBM-Based Breast Cancer Detection Technique Using Mammography Images." Diagnostics 14, no. 2 (2024): 227. http://dx.doi.org/10.3390/diagnostics14020227.

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Breast cancer (BC) is the leading cause of mortality among women across the world. Earlier screening of BC can significantly reduce the mortality rate and assist the diagnostic process to increase the survival rate. Researchers employ deep learning (DL) techniques to detect BC using mammogram images. However, these techniques are resource-intensive, leading to implementation complexities in real-life environments. The performance of convolutional neural network (CNN) models depends on the quality of mammogram images. Thus, this study aimed to build a model to detect BC using a DL technique. Im

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Liao, Caiqing, Xin Wen, Shuman Qi, Yanan Liu, and Rui Cao. "FSE-Net: feature selection and enhancement network for mammogram classification." Physics in Medicine & Biology 68, no. 19 (2023): 195001. http://dx.doi.org/10.1088/1361-6560/acf559.

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Abstract Objective. Early detection and diagnosis allow for intervention and treatment at an early stage of breast cancer. Despite recent advances in computer aided diagnosis systems based on convolutional neural networks for breast cancer diagnosis, improving the classification performance of mammograms remains a challenge due to the various sizes of breast lesions and difficult extraction of small lesion features. To obtain more accurate classification results, many studies choose to directly classify region of interest (ROI) annotations, but labeling ROIs is labor intensive. The purpose of

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Coto Santiesteban, Ariel, Lisbel Garzón Cutiño, and Damian Valdés Santiago. "Deep learning techniques for breast mass malignancy classification on digital mammography." Salud, Ciencia y Tecnología - Serie de Conferencias 4 (January 1, 2025): 669. https://doi.org/10.56294/sctconf2025669.

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Introduction: Breast cancer is one of the most common type of cancer with a high mortality rate. Mammography is widely used to identify breast cancer. Computer Aided Diagnosis systems are used for automatic detection of breast lesions. Methods: We propose and evaluate a deep learning model, called VGG16-C300, for breast mass malignancy classification. CBIS-DDSM dataset was used for training and evaluation. Image contrast enhancement methods like CLAHE and Mean Blur where previously applied to regions of interests. Results: The trained model achieved and area under the curve of 0.80, after 10 i

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Jaamour, Adam, Craig Myles, Ashay Patel, Shuen-Jen Chen, Lewis McMillan, and David Harris-Birtill. "A divide and conquer approach to maximise deep learning mammography classification accuracies." PLOS ONE 18, no. 5 (2023): e0280841. http://dx.doi.org/10.1371/journal.pone.0280841.

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Breast cancer claims 11,400 lives on average every year in the UK, making it one of the deadliest diseases. Mammography is the gold standard for detecting early signs of breast cancer, which can help cure the disease during its early stages. However, incorrect mammography diagnoses are common and may harm patients through unnecessary treatments and operations (or a lack of treatment). Therefore, systems that can learn to detect breast cancer on their own could help reduce the number of incorrect interpretations and missed cases. Various deep learning techniques, which can be used to implement

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Shivani pal, Shivani pal. "ntegrating Mammographic Breast Arterial Calcifications and Clinical Data with Multi-Modal Transformers and GATs for Cardiovascular Risk Prediction." International Journal of Pharmaceutical Research and Applications 10, no. 3 (2025): 1815–28. https://doi.org/10.35629/4494-100318151828.

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Cardiovascular disease (CVD) remains the leading global cause of mortality, necessitating innovative, non-invasive screening methods. Breast arterial calcifications (BAC), visible inmammograms, serve as a robust biomarker for CVD risk.Thisstudyproposesamultimodaldeeplearning framework integrating mammogram analysis with clinical data, using Swin Transformer V2 for image processingandGraphAttentionNetworks(GATs)for clinical data modeling, with late fusion for preciserisk stratification. The dataset combines INbreast, CBIS-DDSM, and a private hospital dataset, totaling 15,000 mammograms with cli

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Kumar Singh, Koushlendra, Suraj Kumar, Marios Antonakakis, et al. "Deep Learning Capabilities for the Categorization of Microcalcification." International Journal of Environmental Research and Public Health 19, no. 4 (2022): 2159. http://dx.doi.org/10.3390/ijerph19042159.

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Breast cancer is the most common cancer in women worldwide. It is the most frequently diagnosed cancer among women in 140 countries out of 184 reporting countries. Lesions of breast cancer are abnormal areas in the breast tissues. Various types of breast cancer lesions include (1) microcalcifications, (2) masses, (3) architectural distortion, and (4) bilateral asymmetry. Microcalcification can be classified as benign, malignant, and benign without a callback. In the present manuscript, we propose an automatic pipeline for the detection of various categories of microcalcification. We performed

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Al-Mansour, Ebtihal, Muhammad Hussain, Hatim A. Aboalsamh, and Saad A. Al-Ahmadi. "Comprehensive Analysis of Mammography Images Using Multi-Branch Attention Convolutional Neural Network." Applied Sciences 13, no. 24 (2023): 12995. http://dx.doi.org/10.3390/app132412995.

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Breast cancer profoundly affects women’s lives; its early diagnosis and treatment increase patient survival chances. Mammography is a common screening method for breast cancer, and many methods have been proposed for automatic diagnosis. However, most of them focus on single-label classification and do not provide a comprehensive analysis concerning density, abnormality, and severity levels. We propose a method based on the multi-label classification of two-view mammography images to comprehensively diagnose a patient’s condition. It leverages the correlation between density type, lesion type,

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Sarah, Khatun, and Ayan Ghosh Mr. "Breast Cancer Detection from Mammograms Using ResNet-50 Transfer Learning and Physics-Informed Neural Networks." Journal of Advanced Research in Artificial Intelligence & It's Applications 2, no. 3 (2025): 46–51. https://doi.org/10.5281/zenodo.15433574.

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<em>Early and reliable breast cancer detection remains a major challenge in medical imaging. We present a novel hybrid framework that combines a ResNet-50 backbone pretrained on ImageNet with a Physics-Informed Neural Network (PINN) branch enforcing the X-ray attenuation law. Trained end-to-end on the CBIS-DDSM mammography dataset, the model uses a composite objective: binary cross-entropy for lesion classification and a physics loss derived from the Beer–Lambert law applied to predicted attenuation coefficients. Data preprocessing includes orientation standardization, ROI cropping, augm

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Rehman, Khalil ur, Jianqiang Li, Yan Pei, Anaa Yasin, Saqib Ali, and Yousaf Saeed. "Architectural Distortion-Based Digital Mammograms Classification Using Depth Wise Convolutional Neural Network." Biology 11, no. 1 (2021): 15. http://dx.doi.org/10.3390/biology11010015.

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Architectural distortion is the third most suspicious appearance on a mammogram representing abnormal regions. Architectural distortion (AD) detection from mammograms is challenging due to its subtle and varying asymmetry on breast mass and small size. Automatic detection of abnormal ADs regions in mammograms using computer algorithms at initial stages could help radiologists and doctors. The architectural distortion star shapes ROIs detection, noise removal, and object location, affecting the classification performance, reducing accuracy. The computer vision-based technique automatically remo

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Talaat, Fatma M., Samah A. Gamel, Rana Mohamed El-Balka, Mohamed Shehata, and Hanaa ZainEldin. "Grad-CAM Enabled Breast Cancer Classification with a 3D Inception-ResNet V2: Empowering Radiologists with Explainable Insights." Cancers 16, no. 21 (2024): 3668. http://dx.doi.org/10.3390/cancers16213668.

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Breast cancer (BCa) poses a severe threat to women’s health worldwide as it is the most frequently diagnosed type of cancer and the primary cause of death for female patients. The biopsy procedure remains the gold standard for accurate and effective diagnosis of BCa. However, its adverse effects, such as invasiveness, bleeding, infection, and reporting time, keep this procedure as a last resort for diagnosis. A mammogram is considered the routine noninvasive imaging-based procedure for diagnosing BCa, mitigating the need for biopsies; however, it might be prone to subjectivity depending on the

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K. Geetha. "Estimation of Breast Cancer with a Combined Feature Selection Algorithm." Journal of Innovative Image Processing 4, no. 3 (2022): 206–14. http://dx.doi.org/10.36548/jiip.2022.3.008.

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Image features are considered as a parametric factor that contains some of the specific information about the given image. In simple terms, a feature can be either a size or resolution or color information of an image. From the observed feature, a computer system can predict the nature of the image same as that of a human’s perception. In the beginning, the image processing algorithms utilized the features of the image only for the preprocessing and segmentation kinds of applications. An information regarding the noise ratio is considered for the preprocessing work to estimate the amount of sm

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Blahová, Linda, Jozef Kostolný, and Ivan Cimrák. "Neural Network-Based Mammography Analysis: Augmentation Techniques for Enhanced Cancer Diagnosis—A Review." Bioengineering 12, no. 3 (2025): 232. https://doi.org/10.3390/bioengineering12030232.

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Application of machine learning techniques in breast cancer detection has significantly advanced due to the availability of annotated mammography datasets. This paper provides a review of mammography studies using key datasets such as CBIS-DDSM, VinDr-Mammo, and CSAW-CC, which play a critical role in training classification and detection models. The analysis of the studies produces a set of data augmentation techniques in mammography, and their impact and performance improvements in detecting abnormalities in breast tissue are studied. The study discusses the challenges of dataset imbalances a

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Al-Tam, Riyadh M., Aymen M. Al-Hejri, Sachin M. Narangale, et al. "A Hybrid Workflow of Residual Convolutional Transformer Encoder for Breast Cancer Classification Using Digital X-ray Mammograms." Biomedicines 10, no. 11 (2022): 2971. http://dx.doi.org/10.3390/biomedicines10112971.

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Breast cancer, which attacks the glandular epithelium of the breast, is the second most common kind of cancer in women after lung cancer, and it affects a significant number of people worldwide. Based on the advantages of Residual Convolutional Network and the Transformer Encoder with Multiple Layer Perceptron (MLP), this study proposes a novel hybrid deep learning Computer-Aided Diagnosis (CAD) system for breast lesions. While the backbone residual deep learning network is employed to create the deep features, the transformer is utilized to classify breast cancer according to the self-attenti

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Mohiyuddin, Aqsa, Asma Basharat, Usman Ghani, et al. "Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network." Computational and Mathematical Methods in Medicine 2022 (January 4, 2022): 1–16. http://dx.doi.org/10.1155/2022/1359019.

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Breast cancer incidence has been rising steadily during the past few decades. It is the second leading cause of death in women. If it is diagnosed early, there is a good possibility of recovery. Mammography is proven to be an excellent screening technique for breast tumor diagnosis, but its detection and classification in mammograms remain a significant challenge. Previous studies’ major limitation is an increase in false positive ratio (FPR) and false negative ratio (FNR), as well as a drop in Matthews correlation coefficient (MCC) value. A model that can lower FPR and FNR while increasing MC

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Oza, Parita Rajiv, Paawan Sharma, and Samir Patel. "A Transfer Representation Learning Approach for Breast Cancer Diagnosis from Mammograms using EfficientNet Models." Scalable Computing: Practice and Experience 23, no. 2 (2022): 51–58. http://dx.doi.org/10.12694/scpe.v23i2.1975.

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Breast cancer is a deadly disease that affects the lives of millions of women throughout the world. Over time, the number of cases of breast cancer has increased. Preventing this disease is difficult and remains unidentified, but the survival percentage can be improved if diagnosed early. The progress of computer-assisted diagnosis (CAD) of breast cancer has seen a lot of improvements thanks to advances in deep learning. With the notable advancement of deep neural networks, diagnostic capabilities are nearing a human expert's. In this paper, we used EfficientNet to classify mammograms. This mo

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Oliveira, Felipe Victor de Sá, and Anthony Lins. "LUISA: Uma Proposta de Ferramenta para Auxílio Ao Diagnóstico do Câncer de Mama a Partir de Imagens de Mamografias Digitalizadas." Revista de Engenharia e Pesquisa Aplicada 5, no. 2 (2020): 73–83. http://dx.doi.org/10.25286/repa.v5i2.1359.

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Problema crescente no mundo, o câncer de mama é considerado um dos principais causadores de mortes em mulheres. A mamografia digital é o principal método de detecção precoce deste tipo de câncer, porém sua interpretação é difícil até mesmo para um profissional. Técnicas de aprendizado de máquina são utilizadas para facilitar esta interpretação. Assim, o presente trabalho tem como objetivo propor um sistema de detecção auxiliado por computador para colaborar com profissionais no diagnóstico do câncer de mama, a partir da análise de imagens de mamografias digitalizadas. Candidatos à lesão foram

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Pavithra M, A N Vinodhini, A. Devendhiran, Saradha S, Gowrishankar Jayaraman,. "Breast Cancer Mammography Classification Using Convolutional Neural Networks and WOA-MPA Optimization." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (2023): 2859–73. http://dx.doi.org/10.52783/tjjpt.v44.i4.1376.

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Breast cancer analysis is crucial for female well-being and mortality statistics. Digital mammograms improve accuracy and survival rates. This study introduces a deep-learning algorithm-based training approach for automated breast cancer identification at early stages, enhancing edge detail and reducing false positives.The proposed methodology involves the integration of a Convolutional Neural Network (CNN) with an optimization strategy to establish a classification model for the diagnosis of breast cancer. The current study utilized a hybrid approach involving the Marine Predators Algorithm (

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Wang, Huina, Lan Wei, Bo Liu, et al. "Transformer-Based Explainable Model for Breast Cancer Lesion Segmentation." Applied Sciences 15, no. 3 (2025): 1295. https://doi.org/10.3390/app15031295.

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Breast cancer is one of the most prevalent cancers among women, with early detection playing a critical role in improving survival rates. This study introduces a novel transformer-based explainable model for breast cancer lesion segmentation (TEBLS), aimed at enhancing the accuracy and interpretability of breast cancer lesion segmentation in medical imaging. TEBLS integrates a multi-scale information fusion approach with a hierarchical vision transformer, capturing both local and global features by leveraging the self-attention mechanism. This model addresses the limitations of existing segmen

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Quintana, Gonzalo Iñaki, Zhijin Li, Laurence Vancamberg, Mathilde Mougeot, Agnès Desolneux, and Serge Muller. "Exploiting Patch Sizes and Resolutions for Multi-Scale Deep Learning in Mammogram Image Classification." Bioengineering 10, no. 5 (2023): 534. http://dx.doi.org/10.3390/bioengineering10050534.

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Recent progress in deep learning (DL) has revived the interest on DL-based computer aided detection or diagnosis (CAD) systems for breast cancer screening. Patch-based approaches are one of the main state-of-the-art techniques for 2D mammogram image classification, but they are intrinsically limited by the choice of patch size, as there is no unique patch size that is adapted to all lesion sizes. In addition, the impact of input image resolution on performance is not yet fully understood. In this work, we study the impact of patch size and image resolution on the classifier performance for 2D

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Aliniya, Parvaneh, Mircea Nicolescu, Monica Nicolescu, and George Bebis. "Improved Loss Function for Mass Segmentation in Mammography Images Using Density and Mass Size." Journal of Imaging 10, no. 1 (2024): 20. http://dx.doi.org/10.3390/jimaging10010020.

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Mass segmentation is one of the fundamental tasks used when identifying breast cancer due to the comprehensive information it provides, including the location, size, and border of the masses. Despite significant improvement in the performance of the task, certain properties of the data, such as pixel class imbalance and the diverse appearance and sizes of masses, remain challenging. Recently, there has been a surge in articles proposing to address pixel class imbalance through the formulation of the loss function. While demonstrating an enhancement in performance, they mostly fail to address t

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Prokopiou, Ioannis, and Panagiota Spyridonos. "Highlighting the Advanced Capabilities and the Computational Efficiency of DeepLabV3+ in Medical Image Segmentation: An Ablation Study." BioMedInformatics 5, no. 1 (2025): 10. https://doi.org/10.3390/biomedinformatics5010010.

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Background: In clinical practice, identifying the location and extent of tumors and lesions is crucial for disease diagnosis and treatment. Artificial intelligence, particularly deep neural networks, offers precise and automated segmentation, yet limited data and high computational demands often hinder its application. Transfer learning helps mitigate these challenges by significantly reducing computational costs, although applying these models can still be resource intensive. This study aims to present flexible and computationally efficient architecture that leverages transfer learning and de

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Zhu, Minjuan, Lei Zhang, Lituan Wang, Zizhou Wang, Yan Wang, and Guangwu Qian. "Local Extremum Mapping for Weak Supervision Learning on Mammogram Classification and Localization." Bioengineering 12, no. 4 (2025): 325. https://doi.org/10.3390/bioengineering12040325.

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The early and accurate detection of breast lesions through mammography is crucial for improving survival rates. However, the existing deep learning-based methods often rely on costly pixel-level annotations, limiting their scalability in real-world applications. To address this issue, a novel local extremum mapping (LEM) mechanism is proposed for mammogram classification and weakly supervised lesion localization. The proposed method first divides the input mammogram into multiple regions and generates score maps through convolutional neural networks. Then, it identifies the most informative re

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Sun, Yeheng, and Yule Ji. "AAWS-Net: Anatomy-aware weakly-supervised learning network for breast mass segmentation." PLOS ONE 16, no. 8 (2021): e0256830. http://dx.doi.org/10.1371/journal.pone.0256830.

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Accurate segmentation of breast masses is an essential step in computer aided diagnosis of breast cancer. The scarcity of annotated training data greatly hinders the model’s generalization ability, especially for the deep learning based methods. However, high-quality image-level annotations are time-consuming and cumbersome in medical image analysis scenarios. In addition, a large amount of weak annotations is under-utilized which comprise common anatomy features. To this end, inspired by teacher-student networks, we propose an Anatomy-Aware Weakly-Supervised learning Network (AAWS-Net) for ex

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Chowanda, Andry. "Exploring the Best Parameters of Deep Learning for Breast Cancer Classification System." CommIT (Communication and Information Technology) Journal 16, no. 2 (2022): 143–48. http://dx.doi.org/10.21512/commit.v16i2.8174.

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Breast cancer is one of the deadliest cancers in the world. It is essential to detect the signs of cancer as early as possible, to make the survival rate higher. However, detecting the signs of breast cancer using the machine or deep learning algorithms from the diagnostic imaging results is not trivial. Slight changes in the illumination of the scanned area can significantly affect the automatic breast cancer classification process. Hence, the research aims to propose an automatic classifier for breast cancer from digital medical imaging (e.g., Positron Emission Tomography or PET, X-Ray of Ma

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Yu, Xiang, Ziquan Zhu, Yoav Alon, David S. Guttery, and Yudong Zhang. "GFNet: A Deep Learning Framework for Breast Mass Detection." Electronics 12, no. 7 (2023): 1583. http://dx.doi.org/10.3390/electronics12071583.

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Background: Breast mass is one of the main symptoms of breast cancer. Effective and accurate detection of breast masses at an early stage would be of great value for clinical breast cancer analysis. Methods: We developed a novel mass detection framework named GFNet. The GFNet is comprised of three modules, including patch extraction, feature extraction, and mass detection. The developed breast mass detection framework is of high robustness and generality that can be self-adapted to images collected by different imaging devices. The patch-based detection is deployed to improve performance. A no

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Mračko, Adam, Lucia Vanovčanová, and Ivan Cimrák. "Mammography Datasets for Neural Networks—Survey." Journal of Imaging 9, no. 5 (2023): 95. http://dx.doi.org/10.3390/jimaging9050095.

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Deep neural networks have gained popularity in the field of mammography. Data play an integral role in training these models, as training algorithms requires a large amount of data to capture the general relationship between the model’s input and output. Open-access databases are the most accessible source of mammography data for training neural networks. Our work focuses on conducting a comprehensive survey of mammography databases that contain images with defined abnormal areas of interest. The survey includes databases such as INbreast, the Curated Breast Imaging Subset of Digital Database

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Rehman, Shams ur, Muhamamd Attique Khan, Anum Masood, et al. "BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images." Diagnostics 13, no. 9 (2023): 1618. http://dx.doi.org/10.3390/diagnostics13091618.

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The early detection of breast cancer using mammogram images is critical for lowering women’s mortality rates and allowing for proper treatment. Deep learning techniques are commonly used for feature extraction and have demonstrated significant performance in the literature. However, these features do not perform well in several cases due to redundant and irrelevant information. We created a new framework for diagnosing breast cancer using entropy-controlled deep learning and flower pollination optimization from the mammogram images. In the proposed framework, a filter fusion-based method for c

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Yeh, Wei-Chang, Wei-Chung Shia, Yun-Ting Hsu, Chun-Hui Huang, and Yong-Shiuan Lee. "A Lightweight Breast Cancer Mass Classification Model Utilizing Simplified Swarm Optimization and Knowledge Distillation." Bioengineering 12, no. 6 (2025): 640. https://doi.org/10.3390/bioengineering12060640.

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In recent years, an increasing number of women worldwide have been affected by breast cancer. Early detection is crucial, as it is the only way to identify abnormalities at an early stage. However, most deep learning models developed for classifying breast cancer abnormalities tend to be large-scale and computationally intensive, often overlooking the constraints of cost and limited computational resources. This research addresses these challenges by utilizing the CBIS-DDSM dataset and introducing a novel concatenated classification architecture and a two-stage strategy to develop an optimized

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Deka, Aniruddha, Debashis Dev Misra, Anindita Das, and Manob Jyoti Saikia. "Breast Cancer Classification via a High-Precision Hybrid IGWO–SOA Optimized Deep Learning Framework." AI 6, no. 8 (2025): 167. https://doi.org/10.3390/ai6080167.

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Breast cancer (BRCA) remains a significant cause of mortality among women, particularly in developing and underdeveloped regions, where early detection is crucial for effective treatment. This research introduces an innovative hybrid model that combines Improved Grey Wolf Optimizer (IGWO) with the Seagull Optimization Algorithm (SOA), forming the IGWO–SOA technique to enhance BRCA detection accuracy. The hybrid model draws inspiration from the adaptive and strategic behaviors of seagulls, especially their ability to dynamically change attack angles in order to effectively tackle complex global

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Mobark, Nada, Safwat Hamad, and S. Z. Rida. "CoroNet: Deep Neural Network-Based End-to-End Training for Breast Cancer Diagnosis." Applied Sciences 12, no. 14 (2022): 7080. http://dx.doi.org/10.3390/app12147080.

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In 2020, according to the publications of both the Global Cancer Observatory (GCO) and the World Health Organization (WHO), breast cancer (BC) represents one of the highest prevalent cancers in women worldwide. Almost 47% of the world’s 100,000 people are diagnosed with breast cancer, among females. Moreover, BC prevails among 38.8% of Egyptian women having cancer. Current deep learning developments have shown the common usage of deep convolutional neural networks (CNNs) for analyzing medical images. Unlike the randomly initialized ones, pre-trained natural image database (ImageNet)-based CNN

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Hazheen Sarbast Mahmood. "Automated Mammogram-Based Breast Cancer Detection with Deep Learning and Advanced Image Enhancement." Journal of Information Systems Engineering and Management 10, no. 45s (2025): 800–810. https://doi.org/10.52783/jisem.v10i45s.9013.

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Breast cancer is a significant worldwide health issue, and early detection is key to enhancing survival. Although mammography is the accepted screening method, human interpretation is susceptible to errors, resulting in misdiagnosis. Convolutional neural networks (CNNs), in particular, have shown promise in deep learning for automating breast cancer detection, increasing accuracy, and reducing human variability. In this research, a deep learning model for automatically classifying breast cancer from mammograms is proposed and evaluated. The suggested model's performance on the CBIS-DDSM datase

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Ahmed, Md Redwan, Hamdadur Rahman, Zishad Hossain Limon, et al. "Hierarchical Swin Transformer Ensemble with Explainable AI for Robust and Decentralized Breast Cancer Diagnosis." Bioengineering 12, no. 6 (2025): 651. https://doi.org/10.3390/bioengineering12060651.

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Early and accurate detection of breast cancer is essential for reducing mortality rates and improving clinical outcomes. However, deep learning (DL) models used in healthcare face significant challenges, including concerns about data privacy, domain-specific overfitting, and limited interpretability. To address these issues, we propose BreastSwinFedNetX, a federated learning (FL)-enabled ensemble system that combines four hierarchical variants of the Swin Transformer (Tiny, Small, Base, and Large) with a Random Forest (RF) meta-learner. By utilizing FL, our approach ensures collaborative model

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Nazir, Muhammad Saquib, Usman Ghani Khan, Aqsa Mohiyuddin, et al. "A Novel CNN-Inception-V4-Based Hybrid Approach for Classification of Breast Cancer in Mammogram Images." Wireless Communications and Mobile Computing 2022 (July 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/5089078.

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Breast cancer is the most frequent disease in women, with one in every 19 women at risk. Breast cancer is the fifth leading cause of cancer death in women around the world. The most effective and efficient technique of controlling cancer development is early identification. Mammography helps in the early detection of cancer, which saves lives. Many studies conducted various tests to categorize the tumor and obtained positive findings. However, there are certain limits. Mass categorization in mammography is still a problem, although it is critical in aiding radiologists in establishing correct

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He, Jingzhen, Jing Wang, Zeyu Han, Baojun Li, Mei Lv, and Yunfeng Shi. "Cancer detection for small-size and ambiguous tumors based on semantic FPN and transformer." PLOS ONE 18, no. 2 (2023): e0275194. http://dx.doi.org/10.1371/journal.pone.0275194.

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Early detection of tumors has great significance for formative detection and determination of treatment plans. However, cancer detection remains a challenging task due to the interference of diseased tissue, the diversity of mass scales, and the ambiguity of tumor boundaries. It is difficult to extract the features of small-sized tumors and tumor boundaries, so semantic information of high-level feature maps is needed to enrich the regional features and local attention features of tumors. To solve the problems of small tumor objects and lack of contextual features, this paper proposes a novel

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Stefano, Alessandro, Fabiano Bini, Eleonora Giovagnoli, et al. "Comparative Evaluation of Machine Learning-Based Radiomics and Deep Learning for Breast Lesion Classification in Mammography." Diagnostics 15, no. 8 (2025): 953. https://doi.org/10.3390/diagnostics15080953.

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Background: Breast cancer is the second leading cause of cancer-related mortality among women, accounting for 12% of cases. Early diagnosis, based on the identification of radiological features, such as masses and microcalcifications in mammograms, is crucial for reducing mortality rates. However, manual interpretation by radiologists is complex and subject to variability, emphasizing the need for automated diagnostic tools to enhance accuracy and efficiency. This study compares a radiomics workflow based on machine learning (ML) with a deep learning (DL) approach for classifying breast lesion

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Srinivashini, N., M. Raveenthini, and R. Lavanya. "Deep ensemble of texture maps for false positive reduction in mammograms." Journal of Physics: Conference Series 2318, no. 1 (2022): 012038. http://dx.doi.org/10.1088/1742-6596/2318/1/012038.

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Abstract Worldwide, breast cancer is a life-threatening disease attributing to increased mortality rates among women. Mammograms are commonly used for screening breast cancer in asymptomatic stages. However, the subtle nature of abnormalities in early stages makes mammogram analysis a cumbersome task. A computer aided diagnosis (CAD) system can complement subjective diagnosis of physicians with its objective assessment. Mass detection is the most important task in breast cancer diagnosis, as masses are the prominent indicators of the disease. Nevertheless, it is the most challenging task due t

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