ReviewComputational and mathematical methods in medicine2019
A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis.
Review in Computational and mathematical methods in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic value of serum HER2 levels in breast cancer: a systematic review and meta-analysis.BMC cancer · 2020Pooled it
- Ultrasound-MRI Fusion Deep Learning Radiomics Nomogram: Refining Prognostic Stratification for Triple-Negative Breast Cancer Treated With Neoadjuvant Chemotherapy.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2026Article
- Infrared spectroscopy with statistical analysis and machine learning for cancer risk assessment in inflammatory bowel disease patients.The Analyst · 2026Article
- Electrochemical aptasensing of HER2 breast cancer biomarker using a Ni nanofoam/Ag-Au ternary nanocomposite.Analytical and bioanalytical chemistry · 2026Article
- A Deep Learning-Based Model Approach for Quantitative Analysis of Cell Chemotaxis in a Microfluidic Chip.Sensors (Basel, Switzerland) · 2025Article
- An approach for classification of breast cancer using lightweight deep convolution neural network.Heliyon · 2024Article
- Article
- CSA-Net: Channel and Spatial Attention-Based Network for Mammogram and Ultrasound Image Classification.Journal of imaging · 2024Article
- Ag nanorod@PEI-Ag nanohybrid as an excellent signal label for sensitive and rapid detection of serum HER2.Scientific reports · 2023Article
- Computational Intelligence in Cancer Diagnostics: A Contemporary Review of Smart Phone Apps, Current Problems, and Future Research Potentials.Diagnostics (Basel, Switzerland) · 2023Review
- The diagnosis of femoroacetabular impingement can be made on pelvis radiographs using deep learning methods.Joint diseases and related surgery · 2023Article
- Accurate breast cancer diagnosis using a stable feature ranking algorithm.BMC medical informatics and decision making · 2023Article
- A Survey of Convolutional Neural Network in Breast Cancer.Computer modeling in engineering & sciences : CMES · 2023Article
- Neural Network in the Analysis of the MR Signal as an Image Segmentation Tool for the Determination of TInternational journal of molecular sciences · 2023Article
- Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer.Asian Pacific journal of cancer prevention : APJCP · 2022Article
- Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis.Genomics, proteomics & bioinformatics · 2022Review
- Improving mammography lesion classification by optimal fusion of handcrafted and deep transfer learning features.Physics in medicine and biology · 2022Article
- Automatic Breast Mass Segmentation and Classification Using Subtraction of Temporally Sequential Digital Mammograms.IEEE journal of translational engineering in health and medicine · 2022Article
- A Survey on Human Cancer Categorization Based on Deep Learning.Frontiers in artificial intelligence · 2022Review
- Comparison of Clinical Efficacy of Sodium Nitroprusside and Urapidil in the Treatment of Acute Hypertensive Cerebral Hemorrhage.Journal of healthcare engineering · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study reviews the technique of convolutional neural network (CNN) applied in a specific field of mammographic breast cancer diagnosis (MBCD). It aims to provide several clues on how to use CNN for related tasks. MBCD is a long-standing problem, and massive computer-aided diagnosis models have been proposed. The models of CNN-based MBCD can be broadly categorized into three groups. One is to design shallow or to modify existing models to decrease the time cost as well as the number of instances for training; another is to make the best use of a pretrained CNN by transfer learning and fine-tuning; the third is to take advantage of CNN models for feature extraction, and the differentiation of malignant lesions from benign ones is fulfilled by using machine learning classifiers. This study enrolls peer-reviewed journal publications and presents technical details and pros and cons of each model. Furthermore, the findings, challenges and limitations are summarized and some clues on the future work are also given. Conclusively, CNN-based MBCD is at its early stage, and there is still a long way ahead in achieving the ultimate goal of using deep learning tools to facilitate clinical practice. This review benefits scientific researchers, industrial engineers, and those who are devoted to intelligent cancer diagnosis.
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Registered trials
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.