ArticleDiagnostics (Basel, Switzerland)2024
Attention-Based Deep Learning Approach for Breast Cancer Histopathological Image Multi-Classification.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- MSWA-ResNet: Multi-Scale Wavelet Attention for Patient-Level and Interpretable Breast Cancer Histopathology Classification.Journal of imaging · 2026Article
- An Interpretable Ensemble Transformer Framework for Breast Cancer Detection in Ultrasound Images.Diagnostics (Basel, Switzerland) · 2026Article
- Cross-Architecture Knowledge Distillation for Histopathological Image Analysis.IEEE access : practical innovations, open solutions · 2026Article
- An attention-based transfer learning framework for breast cancer classification in mammography under limited data conditions.Frontiers in medical technology · 2026Article
- MLHNet-Lung: an attention-guided multi-level CNN-transformer fusion framework with CBAM and GeM pooling for explainable multiclass lung CT image classification.Frontiers in medicine · 2026Article
- Breast cancer diagnosis from histopathological images and molecular signatures by fusing features with an explainable AI-based residual tabular network model.Journal of computer-aided molecular design · 2025Article
- Breast Cancer Histopathological Image Classification Based on Graph Assisted Global Reasoning.Journal of imaging informatics in medicine · 2025Article
- Multi-Class Classification of Breast Cancer Subtypes Using ResNet Architectures on Histopathological Images.Journal of imaging · 2025Article
- Uncovering subtype-specific metabolic signatures in breast cancer through multimodal integration, attention-based deep learning, and self-organizing maps.Scientific reports · 2025Article
- AI in Medical Imaging and Image Processing.Journal of clinical medicine · 2025Article
- Leveraging Attention-Based Deep Learning in Binary Classification for Early-Stage Breast Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- An artificial intelligence model for early-stage breast cancer classification from histopathological biopsy images.Frontiers in artificial intelligence · 2025Article
- Prognostic and predictive value of pathohistological features in gastric cancer and identification of SLITRK4 as a potential biomarker for gastric cancer.Scientific reports · 2024Article
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Authors and funding
3 authors.
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Abstract
Breast cancer diagnosis from histopathology images is often time consuming and prone to human error, impacting treatment and prognosis. Deep learning diagnostic methods offer the potential for improved accuracy and efficiency in breast cancer detection and classification. However, they struggle with limited data and subtle variations within and between cancer types. Attention mechanisms provide feature refinement capabilities that have shown promise in overcoming such challenges. To this end, this paper proposes the Efficient Channel Spatial Attention Network (ECSAnet), an architecture built on EfficientNetV2 and augmented with a convolutional block attention module (CBAM) and additional fully connected layers. ECSAnet was fine-tuned using the BreakHis dataset, employing Reinhard stain normalization and image augmentation techniques to minimize overfitting and enhance generalizability. In testing, ECSAnet outperformed AlexNet, DenseNet121, EfficientNetV2-S, InceptionNetV3, ResNet50, and VGG16 in most settings, achieving accuracies of 94.2% at 40×, 92.96% at 100×, 88.41% at 200×, and 89.42% at 400× magnifications. The results highlight the effectiveness of CBAM in improving classification accuracy and the importance of stain normalization for generalizability.
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