ArticleScientific reports2025
Breast cancer classification based on hybrid CNN with LSTM model.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Classification of Salivary Gland Tumors on Ultrasound Using Artificial Intelligence: A Systematic Review and Meta-Analysis.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026Pooled it
- A cross data learning architecture for breast cancer classification using mammograms.Biomedical signal processing and control · 2026Article
- Benign Share Benefit to Malignant: Balanced Mixing on Feature Space for Imbalanced Breast Cancer Classification.Bioengineering (Basel, Switzerland) · 2026Article
- FusionDiff: a dual-path diffusion-based framework for few-shot authenticity analysis of ceramic microstructures.Scientific reports · 2026Article
- A hybrid deep learning framework for accurate breast cancer classification using MRI images.BMC medical informatics and decision making · 2026Article
- Comparing machine learning and deep learning approaches to predicting the seismic response of slab-column connections.Scientific reports · 2026Article
- Deep learning-based early prediction of carotid plaque response to lipid-lowering therapy using longitudinal multimodal ultrasound imaging.Insights into imaging · 2026Article
- Clinically Robust Deep Learning for Contrast-Enhanced Mammography: Multicenter Evaluation Across Convolutional Neural Network Architectures.Bioengineering (Basel, Switzerland) · 2026Article
- A deep learning framework for breast cancer diagnosis using Swin Transformer and Dual-Attention Multi-scale Fusion Network.Scientific reports · 2026Article
- Engineering the Image Representation for Deep Learning in Contrast-Enhanced Mammography: A Systematic Analysis of Preprocessing and Anatomical Masking.Bioengineering (Basel, Switzerland) · 2026Article
- The emerging role of machine learning-based methods in cancer classification using microRNA.Biochemistry and biophysics reports · 2026Review
- A hybrid LSTM-GRU framework for lung cancer classification using GWO-WOA algorithm for hyperparameter tuning and BPSO for feature selection.Scientific reports · 2026Article
- A hybrid bio inspired neural model based on Ropalidia Marginata behavior for multi disease classification.Scientific reports · 2025Article
- Cross-platform multi-cancer histopathology classification using local-window vision transformers.Scientific reports · 2025Article
- Cancer and Aging Biomarkers: Classification, Early Detection Technologies and Emerging Research Trends.Biosensors · 2025Review
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- From Innovation to Application: Can Emerging Imaging Techniques Transform Breast Cancer Diagnosis?Diagnostics (Basel, Switzerland) · 2025Review
- TransBreastNet a CNN transformer hybrid deep learning framework for breast cancer subtype classification and temporal lesion progression analysis.Scientific reports · 2025Article
- COVID-19 mortality and nutrition through predictive modeling and optimization based on grid search.Scientific reports · 2025Article
- Multi-Class Classification of Breast Cancer Subtypes Using ResNet Architectures on Histopathological Images.Journal of imaging · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer (BC) is a global problem, largely due to a shortage of knowledge and early detection. The speed-up process of detection and classification is crucial for effective cancer treatment. Medical image analysis methods and computer-aided diagnosis can enhance this process, providing training and assistance to less experienced clinicians. Deep Learning (DL) models play a great role in accurately detecting and classifying cancer in the huge dataset, especially when dealing with large medical images. This paper presents a novel hybrid model of DL models combined a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for binary breast cancer classification on two datasets available at the Kaggle repository. CNNs extract mammographic features, including spatial hierarchies and malignancy patterns, whereas LSTM networks characterize sequential dependencies and temporal interactions. Our method combines these structures to improve classification accuracy and resilience. We compared the proposed model with other DL models, such as CNN, LSTM, Gated Recurrent Units (GRUs), VGG-16, and RESNET-50. The CNN-LSTM model achieved superior performance with accuracies of 99.17% and 99.90% on the respective datasets. This paper uses prediction evaluation metrics such as accuracy, sensitivity, specificity, F-score, and the AUC curve. The results showed that our model CNN-LSTM can enhance the performance of breast cancer classifiers compared with others with 99.90% accuracy on the second dataset.
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