ArticleMedical sciences (Basel, Switzerland)2025
A Lightweight Cross-Gated Dual-Branch Attention Network for Colon and Lung Cancer Diagnosis from Histopathological Images.
Article in Medical sciences (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- An optimized EfficientNetB7 deep learning model for lung cancer tissue pathology image recognition.Frontiers in oncology · 2026Article
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6 authors.
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Abstract
BACKGROUND/
objectivesAccurate histopathological classification of lung and colon tissues remains difficult due to subtle morphological overlap between benign and malignant regions. Deep learning approaches have advanced diagnostic precision, yet models often lack interpretability or require complex multi-stage pipelines. This study aimed to develop an end-to-end dual-branch attention network capable of achieving high accuracy while preserving computational efficiency and transparency.
methodsThe architecture integrates EfficientNetV2-B0 and MobileNetV3-Small backbones through a cross-gated fusion mechanism that adaptively balances global context and fine structural details. Efficient channel attention and generalized mean pooling enhance discriminative learning without external feature extraction or optimization stages.
resultsThe network achieved 99.84% accuracy, precision, recall, and F1-score, with an MCC of 0.998. Grad-CAM maps showed strong spatial correspondence with diagnostically relevant histological structures.
conclusionsThe end-to-end framework enables the reliable, interpretable, and computationally efficient classification of lung and colon histopathology and has potential applicability to computer-assisted diagnostic workflows.
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