{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T11:59:17Z","timestamp":1791892757739,"version":"4.3.4"},"reference-count":42,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,5,4]],"date-time":"2025-05-04T00:00:00Z","timestamp":1746316800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Animals"],"abstract":"<jats:p>Cataracts are a prevalent cause of vision loss in dogs, and timely diagnosis is essential for effective treatment. This study aimed to develop and evaluate deep learning models to automatically classify canine cataracts from ocular ultrasound images. A dataset of 3155 ultrasound images (comprising 1329 No cataract, 614 Cortical, 1033 Mature, and 179 Hypermature cases) was used to train and validate four widely used deep learning models (AlexNet, EfficientNetB3, ResNet50, and DenseNet161). Data augmentation and normalization techniques were applied to address category imbalance. DenseNet161 demonstrated the best performance, achieving a test accuracy of 92.03% and an F1-score of 0.8744. The confusion matrix revealed that the model attained the highest accuracy for the No cataract category (99.0%), followed by Cortical (90.3%) and Mature (86.5%) cataracts, while Hypermature cataracts were classified with lower accuracy (78.6%). Receiver Operating Characteristic (ROC) curve analysis confirmed strong discriminative ability, with an area under the curve (AUC) of 0.99. Visual interpretation using Gradient-weighted Class Activation Mapping indicated that the model effectively focused on clinically relevant regions. This deep learning-based classification framework shows significant potential for assisting veterinarians in diagnosing cataracts, thereby improving clinical decision-making in veterinary ophthalmology.<\/jats:p>","DOI":"10.3390\/ani15091327","type":"journal-article","created":{"date-parts":[[2025,5,4]],"date-time":"2025-05-04T20:09:14Z","timestamp":1746389354000},"page":"1327","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Deep Learning-Based Classification of Canine Cataracts from Ocular B-Mode Ultrasound Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Sanghyeon","family":"Park","sequence":"first","affiliation":[{"name":"Helix Animal Medical Center, Seoul 06546, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seokmin","family":"Go","sequence":"additional","affiliation":[{"name":"Nowon N Animal Medical Center, Seoul 01704, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seonhyo","family":"Kim","sequence":"additional","affiliation":[{"name":"Ilsan Animal Medical Center, Ilsan 10368, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaeho","family":"Shim","sequence":"additional","affiliation":[{"name":"Institute of Animal Medicine, College of Veterinary Medicine, Gyeongsang National University, Jinju 52828, Republic of Korea"},{"name":"Department of Veterinary Ophthalmology, College of Veterinary Medicine, Gyeongsang National University, Jinju 52828, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107442","DOI":"10.1016\/j.knosys.2021.107442","article-title":"Cataract detection based on ocular B-ultrasound images by collaborative monitoring deep learning","volume":"231","author":"Wang","year":"2021","journal-title":"Knowl. 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