ArticlePloS one2021
Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN).
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 127 citations in OpenAlex.
- Application of deep learning technology in breast cancer: a systematic review of segmentation, detection, and classification approaches.Biomedical engineering online · 2026Pooled it
- Fully automatic AI diagnosis of alveolar bone defect in anterior teeth on CBCT images.Clinical oral investigations · 2026Article
- AI-assisted multimodal data integration for precision oncology.Frontiers in artificial intelligence · 2026Review
- Recent Advances and Emerging Directions in Machine Learning-Based Breast Cancer Drug Discovery: A Comprehensive Review.Breast cancer (Dove Medical Press) · 2026Review
- HyFusion-X: hybrid deep and traditional feature fusion with ensemble classifiers for breast cancer detection using mammogram and ultrasound images.Scientific reports · 2025Article
- Early breast cancer detection via infrared thermography using a CNN enhanced with particle swarm optimization.Scientific reports · 2025Article
- Multi-Scale Vision Transformer with Optimized Feature Fusion for Mammographic Breast Cancer Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- Mam-Incept-Net: a novel inception model for precise interpretation of mammography images.PeerJ. Computer science · 2025Article
- Convolutional Neural Network-Based Deep Learning Methods for Skeletal Growth Prediction in Dental Patients.Journal of imaging · 2024Article
- CSA-Net: Channel and Spatial Attention-Based Network for Mammogram and Ultrasound Image Classification.Journal of imaging · 2024Article
- Integrated Molecular Docking and Experimental Analysis of Ni(II) Proline Dithiocarbamate Cytotoxicity in MCF-7 Breast Cancer Cells.Asian Pacific journal of cancer prevention : APJCP · 2024Article
- Breast Cancer Diagnosis Method Based on Cross-Mammogram Four-View Interactive Learning.Tomography (Ann Arbor, Mich.) · 2024Article
- Investigations of Ni(II)Cysteine-Tyrosine Dithiocarbamate Complex: Synthesis, Characterization, Molecular Docking, Molecular Dynamic, and Anticancer Activity on MCF-7 Breast Cancer Cell Line.Asian Pacific journal of cancer prevention : APJCP · 2024Article
- Article
- A review of the machine learning datasets in mammography, their adherence to the FAIR principles and the outlook for the future.Scientific data · 2023Review
- Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural Networks.Diagnostics (Basel, Switzerland) · 2023Article
- Benign and Malignant Breast Tumor Classification in Ultrasound and Mammography Images via Fusion of Deep Learning and Handcraft Features.Entropy (Basel, Switzerland) · 2023Article
- A Survey of Convolutional Neural Network in Breast Cancer.Computer modeling in engineering & sciences : CMES · 2023Article
- Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The real cause of breast cancer is very challenging to determine and therefore early detection of the disease is necessary for reducing the death rate due to risks of breast cancer. Early detection of cancer boosts increasing the survival chance up to 8%. Primarily, breast images emanating from mammograms, X-Rays or MRI are analyzed by radiologists to detect abnormalities. However, even experienced radiologists face problems in identifying features like micro-calcifications, lumps and masses, leading to high false positive and high false negative. Recent advancement in image processing and deep learning create some hopes in devising more enhanced applications that can be used for the early detection of breast cancer. In this work, we have developed a Deep Convolutional Neural Network (CNN) to segment and classify the various types of breast abnormalities, such as calcifications, masses, asymmetry and carcinomas, unlike existing research work, which mainly classified the cancer into benign and malignant, leading to improved disease management. Firstly, a transfer learning was carried out on our dataset using the pre-trained model ResNet50. Along similar lines, we have developed an enhanced deep learning model, in which learning rate is considered as one of the most important attributes while training the neural network. The learning rate is set adaptively in our proposed model based on changes in error curves during the learning process involved. The proposed deep learning model has achieved a performance of 88% in the classification of these four types of breast cancer abnormalities such as, masses, calcifications, carcinomas and asymmetry mammograms.
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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.