{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T05:04:47Z","timestamp":1790053487916,"version":"4.0.1"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007452","name":"Wu Jieping Medical Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007452","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Lung cancer is the leading cause of cancer deaths worldwide. This study constructed and optimized a deep learning model to recognize lung cancer pathological images for intelligent classification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The LC25000 dataset was adopted for model training, and the LungHist700 dataset served as independent validation, with validation F1 score as the primary evaluation metric. Nine classic convolutional neural networks (DenseNet201, EfficientNetB7, EfficientNetV2S, InceptionResNetV2, MobileNetV3Large, NASNetLarge, ResNet50V2, VGG19, and Xception) were first benchmarked. Afterwards, three ensemble strategies including Equal-Weighted Averaging, Performance-Weighted Averaging and XGBoost were tested. Meanwhile, hyperparameter tuning was implemented on the optimal single model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Result<\/jats:title>\n                    <jats:p>All nine baseline models obtained good performance on training and internal test datasets. Among individual models, EfficientNetB7 performed best with an F1 score of 0.891 and AUC of 0.749. Ensemble methods only reached a maximum F1 of 0.889, showing no obvious advantage over the best single model. By contrast, the hyperparameter-optimized EfficientNetB7 achieved remarkable generalization improvement, with accuracy 0.873, precision 0.864, recall 0.999, F1 score0.926 and AUC 0.871.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The optimized model effectively recognizes lung cancer pathological images and supplies technical support for intelligent diagnosis. Multi-center large-sample clinical validation is required for future clinical transformation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1862407","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T04:21:10Z","timestamp":1790050870000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["An optimized EfficientNetB7 deep learning model for lung cancer tissue pathology image recognition"],"prefix":"10.3389","volume":"16","author":[{"given":"Xiongwen","family":"He","sequence":"first","affiliation":[{"name":"Department of Medical Oncology, The Second Affiliated Hospital of Hainan Medical University","place":["Haikou, Hainan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lili","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology, The Second Affiliated Hospital of Hainan Medical 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