ArticleDiagnostics (Basel, Switzerland)2022
Breast Cancer Mammograms Classification Using Deep Neural Network and Entropy-Controlled Whale Optimization Algorithm.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- CNN Sample-Size Effects Across Biomedical Datasets: A Reliability Pattern in Overfitting, Ranking, and Monotonicity.Bioengineering (Basel, Switzerland) · 2026Article
- Enhancing Breast Density Assessment in Mammograms Through Artificial Intelligence.Journal of imaging informatics in medicine · 2026Article
- A Novel Hybrid CNN-ViT-Based Bi-Directional Cross-Guidance Fusion-Driven Breast Cancer Detection Model.Life (Basel, Switzerland) · 2026Article
- Quantum-SpinalNet: a hybrid deep learning approach for mammographic breast cancer detection.Frontiers in digital health · 2026Article
- Deep learning-based bacterial foraging optimization algorithm to improve digital mammography-based breast cancer detection.Scientific reports · 2025Article
- Innovative Multi-View Strategies for AI-Assisted Breast Cancer Detection in Mammography.Journal of imaging · 2025Article
- Breast lesion classification via colorized mammograms and transfer learning in a novel CAD framework.Scientific reports · 2025Article
- A Lightweight Breast Cancer Mass Classification Model Utilizing Simplified Swarm Optimization and Knowledge Distillation.Bioengineering (Basel, Switzerland) · 2025Article
- Multi-Scale Vision Transformer with Optimized Feature Fusion for Mammographic Breast Cancer Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Integrative hybrid deep learning for enhanced breast cancer diagnosis: leveraging the Wisconsin Breast Cancer Database and the CBIS-DDSM dataset.Scientific reports · 2024Article
- MRI-Driven Alzheimer's Disease Diagnosis Using Deep Network Fusion and Optimal Selection of Feature.Bioengineering (Basel, Switzerland) · 2024Article
- Enhancing Oral Squamous Cell Carcinoma Detection Using Histopathological Images: A Deep Feature Fusion and Improved Haris Hawks Optimization-Based Framework.Bioengineering (Basel, Switzerland) · 2024Article
- A Novel Classification Method: Neighborhood-Based Positive Unlabeled Learning Using Decision Tree (NPULUD).Entropy (Basel, Switzerland) · 2024Article
- Unified deep learning models for enhanced lung cancer prediction with ResNet-50-101 and EfficientNet-B3 using DICOM images.BMC medical imaging · 2024Article
- An Enhanced LightGBM-Based Breast Cancer Detection Technique Using Mammography Images.Diagnostics (Basel, Switzerland) · 2024Article
- Offloading the computational complexity of transfer learning with generic features.PeerJ. Computer science · 2024Article
- Mammography with deep learning for breast cancer detection.Frontiers in oncology · 2024Review
- A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images.Frontiers in oncology · 2024Article
- Enhancing Alzheimer's disease diagnosis and staging: a multistage CNN framework using MRI.Frontiers in psychiatry · 2024Article
- A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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3 authors.
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Abstract
Breast cancer has affected many women worldwide. To perform detection and classification of breast cancer many computer-aided diagnosis (CAD) systems have been established because the inspection of the mammogram images by the radiologist is a difficult and time taken task. To early diagnose the disease and provide better treatment lot of CAD systems were established. There is still a need to improve existing CAD systems by incorporating new methods and technologies in order to provide more precise results. This paper aims to investigate ways to prevent the disease as well as to provide new methods of classification in order to reduce the risk of breast cancer in women's lives. The best feature optimization is performed to classify the results accurately. The CAD system's accuracy improved by reducing the false-positive rates.The Modified Entropy Whale Optimization Algorithm (MEWOA) is proposed based on fusion for deep feature extraction and perform the classification. In the proposed method, the fine-tuned MobilenetV2 and Nasnet Mobile are applied for simulation. The features are extracted, and optimization is performed. The optimized features are fused and optimized by using MEWOA. Finally, by using the optimized deep features, the machine learning classifiers are applied to classify the breast cancer images. To extract the features and perform the classification, three publicly available datasets are used: INbreast, MIAS, and CBIS-DDSM. The maximum accuracy achieved in INbreast dataset is 99.7%, MIAS dataset has 99.8% and CBIS-DDSM has 93.8%. Finally, a comparison with other existing methods is performed, demonstrating that the proposed algorithm outperforms the other approaches.
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