ArticlePloS one2022
Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
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33 citing papers in PubMed, 2 syntheses or guidelines pooled it, 99 citations in OpenAlex.
- Diagnostic accuracy of ovarian cancer using convolutional neural network: a systematic review and meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- A systematic literature review on mammography: deep learning techniques for breast cancer detection with global and Asian perspectives.BMC cancer · 2025Pooled it
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- DeiT-MLP-mixer for the preoperative prediction of axillary lymph node involvement in breast cancer via ultrasound imaging.BMC medical imaging · 2026Article
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Different BI-RADS breast cancer diagnosis using MobileNetV1 and vision transformer based on explainable artificial intelligence (XAI).Scientific reports · 2026Article
- TriFusion-ADFormer: a deep learning framework for early Alzheimer's disease detection using MRI and cognitive metrics.Frontiers in artificial intelligence · 2026Article
- A 2.5D multichannel deep learning model using contrast-enhanced ultrasound for predicting malignancy in breast nodules: a two-center study.Frontiers in physiology · 2026Article
- Benchmark-grounded self-supervised contrastive learning for AMI-based distribution grid monitoring.Frontiers in artificial intelligence · 2026Article
- Breast cancer classification based on microcalcifications using dual branch vision transformer fusion.Scientific reports · 2025Article
- Hybrid deep learning with attention fusion for enhanced colon cancer detection.Scientific reports · 2025Article
- Breast cancer diagnosis from histopathological images and molecular signatures by fusing features with an explainable AI-based residual tabular network model.Journal of computer-aided molecular design · 2025Article
- Deep learning-based bacterial foraging optimization algorithm to improve digital mammography-based breast cancer detection.Scientific reports · 2025Article
- A Deep Learning and Explainable Artificial Intelligence based Scheme for Breast Cancer Detection.Scientific reports · 2025Article
- Robust Bi-CBMSegNet framework for advancing breast mass segmentation in mammography with a dual module encoder-decoder approach.Scientific reports · 2025Article
- Enhancing lung cancer detection through integrated deep learning and transformer models.Scientific reports · 2025Article
- A labeled medical records corpus for the timely detection of rare diseases using machine learning approaches.Scientific reports · 2025Article
- Breast cancer classification based on hybrid CNN with LSTM model.Scientific reports · 2025Article
- Applying YOLOv6 as an ensemble federated learning framework to classify breast cancer pathology images.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is one of the worst illnesses, with a higher fatality rate among women globally. Breast cancer detection needs accurate mammography interpretation and analysis, which is challenging for radiologists owing to the intricate anatomy of the breast and low image quality. Advances in deep learning-based models have significantly improved breast lesions' detection, localization, risk assessment, and categorization. This study proposes a novel deep learning-based convolutional neural network (ConvNet) that significantly reduces human error in diagnosing breast malignancy tissues. Our methodology is most effective in eliciting task-specific features, as feature learning is coupled with classification tasks to achieve higher performance in automatically classifying the suspicious regions in mammograms as benign and malignant. To evaluate the model's validity, 322 raw mammogram images from Mammographic Image Analysis Society (MIAS) and 580 from Private datasets were obtained to extract in-depth features, the intensity of information, and the high likelihood of malignancy. Both datasets are magnificently improved through preprocessing, synthetic data augmentation, and transfer learning techniques to attain the distinctive combination of breast tumors. The experimental findings indicate that the proposed approach achieved remarkable training accuracy of 0.98, test accuracy of 0.97, high sensitivity of 0.99, and an AUC of 0.99 in classifying breast masses on mammograms. The developed model achieved promising performance that helps the clinician in the speedy computation of mammography, breast masses diagnosis, treatment planning, and follow-up of disease progression. Moreover, it has the immense potential over retrospective approaches in consistency feature extraction and precise lesions classification.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.