ArticlePloS one2024
An optimized model based on adaptive convolutional neural network and grey wolf algorithm for breast cancer diagnosis.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Metaheuristic-enhanced deep learning for monthly pan evaporation prediction under limited climatic data.Scientific reports · 2026Article
- Magnification-independent breast cancer diagnosis using a GWO-enhanced vision transformer with multi-stage stain normalization.Scientific reports · 2026Article
- Breast Cancer Classification Using Feature Selection via Improved Simulated Annealing and SVM Classifier.Diagnostics (Basel, Switzerland) · 2026Article
- Cloud-enabled automatic modulation classification using deep feature fusion and Moth-Flame Optimized ELM approach.Scientific reports · 2025Article
- Comparative performance of deep learning models and non-dermatologists in diagnosing psoriasis, dermatophytosis, and eczema.Scientific reports · 2025Article
- Deep learning algorithms for identifying developmental dysplasia of the hip based on sonographic images: a retrospective, prospective, multicenter study in China.EClinicalMedicine · 2025Article
- Multistage feature selection and stacked generalization model for cancer detection.Scientific reports · 2025Article
- Enhancing lymphoma cancer detection using deep transfer learning on histopathological images.Scientific reports · 2025Article
- Deep learning-based bacterial foraging optimization algorithm to improve digital mammography-based breast cancer detection.Scientific reports · 2025Article
- An optimized hybrid deep learning model to detect Alzheimer disease.Scientific reports · 2025Article
- Attention-augmented hybrid CNN-LSTM model for social media sentiment analysis in cryptocurrency investment decision-making.Scientific reports · 2025Article
- GenoDense-Net: unraveling the genomic puzzle of the global pathogen.Tropical diseases, travel medicine and vaccines · 2025Article
- Sentiment analysis with echo state network and augmented water cycle algorithm.Scientific reports · 2025Article
- CHASHNIt for enhancing skin disease classification using GAN augmented hybrid model with LIME and SHAP based XAI heatmaps.Scientific reports · 2025Article
- Deep learning model for early acute lymphoblastic leukemia detection using microscopic images.Scientific reports · 2025Article
- A novel approach for breast cancer detection using a Nesterov accelerated adam optimizer with an attention mechanism.Scientific reports · 2025Article
- A hybrid deep learning model EfficientNet with GRU for breast cancer detection from histopathology images.Scientific reports · 2025Article
- A novel neuroimaging based early detection framework for alzheimer disease using deep learning.Scientific reports · 2025Article
- Federated learning-based CT liver tumor detection using a teacher‒student SANet with semisupervised learning.BMC medical imaging · 2025Article
- Deep learning for gender estimation using hand radiographs: a comparative evaluation of CNN models.BMC medical imaging · 2025Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Medical image classification (IC) is a method for categorizing images according to the appropriate pathological stage. It is a crucial stage in computer-aided diagnosis (CAD) systems, which were created to help radiologists with reading and analyzing medical images as well as with the early detection of tumors and other disorders. The use of convolutional neural network (CNN) models in the medical industry has recently increased, and they achieve great results at IC, particularly in terms of high performance and robustness. The proposed method uses pre-trained models such as Dense Convolutional Network (DenseNet)-121 and Visual Geometry Group (VGG)-16 as feature extractor networks, bidirectional long short-term memory (BiLSTM) layers for temporal feature extraction, and the Support Vector Machine (SVM) and Random Forest (RF) algorithms to perform classification. For improved performance, the selected pre-trained CNN hyperparameters have been optimized using a modified grey wolf optimization method. The experimental analysis for the presented model on the Mammographic Image Analysis Society (MIAS) dataset shows that the VGG16 model is powerful for BC classification with overall accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC) of 99.86%, 99.9%, 99.7%, 97.1%, and 1.0, respectively, on the MIAS dataset and 99.4%, 99.03%, 99.2%, 97.4%, and 1.0, respectively, on the INbreast dataset.
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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.