{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T11:52:16Z","timestamp":1787313136942,"version":"build-2736575974"},"reference-count":33,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2023,11,22]],"date-time":"2023-11-22T00:00:00Z","timestamp":1700611200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aims to develop a ResNet50\u2010based deep learning model for focal liver lesion (FLL) classification in ultrasound images, comparing its performance with other models and prior research.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methodology<\/jats:title>\n                    <jats:p>We retrospectively collected 581 ultrasound images from the Chulabhorn Hospital's HCC surveillance and screening project (2010\u20132018). The dataset comprised five classes: non\u2010FLL, hepatic cyst (Cyst), hemangioma (HMG), focal fatty sparing (FFS), and hepatocellular carcinoma (HCC). We conducted 5\u2010fold cross\u2010validation after random dataset partitioning, enhancing training data with data augmentation. Our models used modified pre\u2010trained ResNet50, GGN, ResNet18, and VGG16 architectures. Model performance, assessed via confusion matrices for sensitivity, specificity, and accuracy, was compared across models and with prior studies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>ResNet50 outperformed other models, achieving a 5\u2010fold cross\u2010validation accuracy of 87\u00a0\u00b1\u00a02.2%. While VGG16 showed similar performance, it exhibited higher uncertainty. In the testing phase, the pretrained ResNet50 excelled in classifying non\u2010FLL, cysts, and FFS. To compare with other research, ResNet50 surpassed the prior methods like two\u2010layered feed\u2010forward neural networks (FFNN) and CNN+ReLU in FLL diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>ResNet50 exhibited good performance in FLL diagnosis, especially for HCC classification, suggesting its potential for developing computer\u2010aided FLL diagnosis. However, further refinement is required for HCC and HMG classification in future studies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.14210","type":"journal-article","created":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T19:19:54Z","timestamp":1700767194000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Diagnosis of focal liver lesions from ultrasound images using a pretrained residual neural network"],"prefix":"10.1002","volume":"25","author":[{"given":"Sutthirak","family":"Tangruangkiat","sequence":"first","affiliation":[{"name":"School of Radiological Technology, Faculty of Health Science Technology Chulabhorn Royal Academy  Bangkok Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Napatsorn","family":"Chaiwongkot","sequence":"additional","affiliation":[{"name":"School of Radiological Technology, Faculty of Health Science Technology Chulabhorn Royal Academy  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