ArticleMedical & biological engineering & computing2020
Deep feature-based automatic classification of mammograms.
Article in Medical & biological engineering & computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 95 citations in OpenAlex.
- Breast cancer pathogenesis, diagnosis and treatment: a comprehensive review.Frontiers in oncology · 2026Review
- HyFusion-X: hybrid deep and traditional feature fusion with ensemble classifiers for breast cancer detection using mammogram and ultrasound images.Scientific reports · 2025Article
- Enhancing Automated Breast Cancer Detection: A CNN-Driven Method for Multi-Modal Imaging Techniques.Journal of personalized medicine · 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
- Machine learning and new insights for breast cancer diagnosis.The Journal of international medical research · 2024Review
- Exploiting Patch Sizes and Resolutions for Multi-Scale Deep Learning in Mammogram Image Classification.Bioengineering (Basel, Switzerland) · 2023Article
- On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks.Archives of computational methods in engineering : state of the art reviews · 2023Article
- The application of traditional machine learning and deep learning techniques in mammography: a review.Frontiers in oncology · 2023Review
- Breast Cancer Detection Using Automated Segmentation and Genetic Algorithms.Diagnostics (Basel, Switzerland) · 2022Article
- Review
- Breast Cancer Mammograms Classification Using Deep Neural Network and Entropy-Controlled Whale Optimization Algorithm.Diagnostics (Basel, Switzerland) · 2022Article
- Automatic Breast Mass Segmentation and Classification Using Subtraction of Temporally Sequential Digital Mammograms.IEEE journal of translational engineering in health and medicine · 2022Article
- Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks based approach.Frontiers in genetics · 2022Article
- Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network.Biosensors · 2021Article
- A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms.Journal of imaging · 2021Review
- Article
- Mammogram mass segmentation and detection using Legendre neural network-based optimal threshold.Medical & biological engineering & computing · 2021Article
- Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Breast cancer has the second highest frequency of death rate among women worldwide. Early-stage prevention becomes complex due to reasons unknown. However, some typical signatures like masses and micro-calcifications upon investigating mammograms can help diagnose women better. Manual diagnosis is a hard task the radiologists carry out frequently. For their assistance, many computer-aided diagnosis (CADx) approaches have been developed. To improve upon the state of the art, we proposed a deep ensemble transfer learning and neural network classifier for automatic feature extraction and classification. In computer-assisted mammography, deep learning-based architectures are generally not trained on mammogram images directly. Instead, the images are pre-processed beforehand, and then they are adopted to be given as input to the ensemble model proposed. The robust features extracted from the ensemble model are optimized into a feature vector which are further classified using the neural network (nntraintool). The network was trained and tested to separate out benign and malignant tumors, thus achieving an accuracy of 0.88 with an area under curve (AUC) of 0.88. The attained results show that the proposed methodology is a promising and robust CADx system for breast cancer classification. Graphical Abstract Flow diagram of the proposed approach. Figure depicts the deep ensemble extracting the robust features with the final classification using neural networks.
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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.