Preliminary exploration on using entropy-weighted hybrid pooling in CNN for ultrasound breast cancer detection

Information from the abstract

Introduction Breast ultrasound imaging is valuable for its non-invasiveness and cost-effectiveness but presents diagnostic challenges from speckle noise and limited contrast. Conventional CNN pooling methods compromise between noise reduction and feature preservation. Methods We introduce an adaptive entropy-weighted hybrid pooling approach that dynamically combines Max and Average pooling based on local image complexity measured by Shannon entropy. Two CNN architectures (3-block and 4-block) were evaluated on a publicly available ultrasound dataset of 9,016 images with an additional 10% speckle-noise variant, using accuracy, precision, recall, F1-score, ROC curves, confusion matrices, and inference time. For the 3-block CNN, results are reported as the mean ± standard deviation over three random seeds. Results In the 3-block CNN, hybrid pooling achieved an accuracy of 93.98% ± 1.72% (AUC = 0.9870), exceeding max pooling (92.72% ± 0.85%, AUC = 0.9815). In the deeper 4-block CNN (single-run), hybrid pooling remained competitive (accuracy 92.90%) relative to max pooling (94.79%). Deeper architectures improved accuracy, noise robustness, and convergence but required careful regularization to avoid overfitting. Discussion This preliminary study highlights the adaptive hybrid pooling's potential in clinical ultrasound breast cancer diagnosis, recommending further validation and clinical integration.

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Related topics: AI in cancer detection · Ultrasound Imaging and Elastography · Advanced Neural Network Applications