{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T12:26:18Z","timestamp":1787401578998,"version":"build-2736575974"},"reference-count":16,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T00:00:00Z","timestamp":1787097600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Ultrason Imaging"],"abstract":"<jats:p>\n                    To compare pykan and Lightweight KAN with MLP and Linear classifier heads for focal liver lesion classification on B-mode ultrasound, with emphasis on diagnostic sensitivity, interpretability and computational cost. A frozen ResNet18 feature extractor was combined with 4 classifier heads and evaluated on 735 annotated liver ultrasound images (Benign, Malignant, Normal) using 5-fold stratified cross-validation. Weighted cross-entropy was applied to address class imbalance. Performance was assessed using Accuracy, F1-Macro, F1-Weighted, AUROC, class-wise recall, malignant-to-normal misclassification count, inference latency, and pykan-specific spline activeness analysis. Overall metrics were similar across models, and Wilcoxon Signed-Rank testing showed no statistically significant pairwise differences (\n                    <jats:italic toggle=\"yes\">p<\/jats:italic>\n                    \u2009&gt;\u2009.05). However, clinically relevant class-level differences emerged. pykan achieved the highest Benign recall (65.5%) and the lowest malignant-to-normal error rate (5\/435, 1.1%). Lightweight KAN achieved the highest Malignant recall (82.1%) and markedly faster inference (0.69\u2009ms\/image) than pykan (4183.67\u2009ms\/image). Aggregate metrics alone do not capture clinically meaningful trade-offs among KAN implementations. Lightweight KAN is attractive for rapid screening, whereas pykan shows a directional trend toward fewer malignant-to-normal errors and a more interpretable profile, though this trend did not reach statistical significance and requires confirmation in larger studies.\n                  <\/jats:p>","DOI":"10.1177\/01617346261476280","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T02:38:41Z","timestamp":1787193521000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating Kolmogorov-Arnold Network Implementations for Medical Ultrasound Image Classification: A Trade-off Analysis of Computational Cost, Interpretability, and Diagnostic Safety"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4104-1928","authenticated-orcid":false,"given":"Saim","family":"Ervural","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronics Engineering, KTO Karatay University, Konya, Turkey"},{"name":"Smart Technologies Design, Development and Prototyping Centre (AKITEK), Konya, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,8,19]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41571-023-00825-3"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhep.2022.08.021"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.14210"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1055\/a-2066-9372"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2023.110932"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-026-02629-y"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.3748\/wjg.v25.i6.672"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.diii.2019.02.009"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TUFFC.2022.3161719"},{"key":"e_1_3_3_12_2","unstructured":"Liu Z Wang Y Vaidya S et al. KAN: Kolmogorov-Arnold networks. In: The Thirteenth International Conference on Learning Representations (ICLR) [Internet]. 2025 https:\/\/openreview.net\/forum?id=Ozo7qJ5vZi"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1101\/2024.06.04.24308428"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.30548"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.7272660"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2018.11.020"}],"container-title":["Ultrasonic Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/01617346261476280","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/01617346261476280","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/01617346261476280","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T11:51:46Z","timestamp":1787399506000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/01617346261476280"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,19]]},"references-count":16,"alternative-id":["10.1177\/01617346261476280"],"URL":"https:\/\/doi.org\/10.1177\/01617346261476280","relation":{},"ISSN":["0161-7346","1096-0910"],"issn-type":[{"value":"0161-7346","type":"print"},{"value":"1096-0910","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,19]]},"article-number":"01617346261476280"}}