{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T05:18:44Z","timestamp":1783487924296,"version":"3.55.0"},"reference-count":24,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T00:00:00Z","timestamp":1783468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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 \u00b1 standard deviation over three random seeds.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the 3-block CNN, hybrid pooling achieved an accuracy of 93.98% \u00b1 1.72% (AUC = 0.9870), exceeding max pooling (92.72% \u00b1 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>This preliminary study highlights the adaptive hybrid pooling's potential in clinical ultrasound breast cancer diagnosis, recommending further validation and clinical integration.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1660518","type":"journal-article","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T04:50:07Z","timestamp":1783486207000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Preliminary exploration on using entropy-weighted hybrid pooling in CNN for ultrasound breast cancer 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