ArticleScientific reports2025
Histopathological image based breast cancer diagnosis using deep learning and bio inspired optimization.
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 4 papers.
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Who cites it
4 citing papers in PubMed.
- Attention-Driven CNNs as a Strong Default for HER2 Prediction from DCE-MRI: A Comparison with Transformer Architectures.Bioengineering (Basel, Switzerland) · 2026Article
- BC-SwinNet: Swin transformer and CNN with multi-objective optimization for multi-class breast cancer detection using Histopathological images.Scientific reports · 2026Article
- Interpretable CRAM‑Enhanced Lightweight Dual‑Branch CNN for Real‑Time Breast Cancer Histopathology in Internet‑of‑Medical‑Things Environments.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Leveraging medical imaging and deep learning for diagnosis of breast cancer using histopathological images.Scientific reports · 2026Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer diagnosis remains a crucial challenge in medical research, necessitating accurate and automated detection methods. This study introduces an advanced deep learning framework for histopathological image classification, integrating AlexNet and Gated Recurrent Unit (GRU) networks, optimized using the Hippopotamus Optimization Algorithm (HOA). Initially, DenseNet-41 extracts intricate spatial features from histopathological images. These features are then processed by the hybrid AlexNet-GRU model, leveraging AlexNet's robust feature extraction and GRU's sequential learning capabilities. HOA is employed to fine-tune hyperparameters, ensuring optimal model performance. The proposed approach is evaluated on benchmark datasets (BreakHis and BACH), achieving a classification accuracy of 99.60%, surpassing existing state-of-the-art models. The results demonstrate the efficacy of integrating deep learning with bio-inspired optimization techniques in breast cancer detection. This research offers a robust and computationally efficient framework for improving early diagnosis and clinical decision-making, potentially enhancing patient outcomes.
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Registered trials
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