ArticlePloS one2020
New convolutional neural network model for screening and diagnosis of mammograms.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 65 citations in OpenAlex.
- From Lab to Clinic: Artificial Intelligence with Spectroscopic Liquid Biopsies.Diagnostics (Basel, Switzerland) · 2025Review
- Improving Benign and Malignant Classifications in Mammography with ROI-Stratified Deep Learning.Bioengineering (Basel, Switzerland) · 2025Article
- A Lightweight Breast Cancer Mass Classification Model Utilizing Simplified Swarm Optimization and Knowledge Distillation.Bioengineering (Basel, Switzerland) · 2025Article
- Hybrid transformer-based model for mammogram classification by integrating prior and current images.Medical physics · 2025Article
- Local Extremum Mapping for Weak Supervision Learning on Mammogram Classification and Localization.Bioengineering (Basel, Switzerland) · 2025Article
- Reproducibility and Explainability of Deep Learning in Mammography: A Systematic Review of Literature.The Indian journal of radiology & imaging · 2024Review
- Clinical application of convolutional neural network for mass analysis on mammograms.Quantitative imaging in medicine and surgery · 2023Article
- Sensitivity of CNN image analysis to multifaceted measurements of neurite growth.BMC bioinformatics · 2023Article
- Exploiting Patch Sizes and Resolutions for Multi-Scale Deep Learning in Mammogram Image Classification.Bioengineering (Basel, Switzerland) · 2023Article
- The application of traditional machine learning and deep learning techniques in mammography: a review.Frontiers in oncology · 2023Review
- Tensor-Based Learning for Detecting Abnormalities on Digital Mammograms.Diagnostics (Basel, Switzerland) · 2022Article
- Deep learning in image-based breast and cervical cancer detection: a systematic review and meta-analysis.NPJ digital medicine · 2022Article
- A Novel Hybrid Convolutional Neural Network Approach for the Stomach Intestinal Early Detection Cancer Subtype Classification.Computational intelligence and neuroscience · 2022Article
- Leveraging Multi-Task Learning to Cope With Poor and Missing Labels of Mammograms.Frontiers in radiology · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is the most common cancer in women and poses a great threat to women's life and health. Mammography is an effective method for the diagnosis of breast cancer, but the results are largely limited by the clinical experience of radiologists. Therefore, the main purpose of this study is to perform two-stage classification (Normal/Abnormal and Benign/Malignancy) of two- view mammograms through convolutional neural network. In this study, we constructed a multi-view feature fusion network model for classification of mammograms from two views, and we proposed a multi-scale attention DenseNet as the backbone network for feature extraction. The model consists of two independent branches, which are used to extract the features of two mammograms from different views. Our work mainly focuses on the construction of multi-scale convolution module and attention module. The final experimental results show that the model has achieved good performance in both classification tasks. We used the DDSM database to evaluate the proposed method. The accuracy, sensitivity and AUC values of normal and abnormal mammograms classification were 94.92%, 96.52% and 94.72%, respectively. And the accuracy, sensitivity and AUC values of benign and malignant mammograms classification were 95.24%, 96.11% and 95.03%, respectively.
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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.