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It mainly appears as cystic echinococcosis (CE), due to Echinococcus granulosus, and alveolar echinococcosis (AE), caused by multilocular Echinococcus species. CE can lead to biliary colic or vascular compression, while AE often mimics liver cancer and exhibits high long\u2010term mortality. Although ultrasonography is the diagnostic method of choice, its accuracy is affected by imaging artifacts and operator variability. Conventional deep learning models are often computationally heavy and lack interpretability, limiting their use in underdeveloped areas. Integrating frequency\u2010 and spatial\u2010domain features in a lightweight framework offers a promising solution for efficient segmentation and classification of hepatic echinococcosis. The aim of this study is to develop efficient deep learning models for hepatic echinococcosis segmentation and classification, facilitating large\u2010scale screening with non\u2010invasive, portable ultrasound imaging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study utilized a large ultrasound dataset to train and evaluate a deep learning model, consisting of 20,112 images from 4437 patients in Shiqu County, Sichuan Province, China, an endemic area for hepatic echinococcosis. To further assess the model's robustness, an external dataset comprising 3340 images from 1123 patients at West China Hospital of Sichuan University was used for additional testing. By enhancing the correlation of image features in both the frequency and spatial domains for hepatic echinococcosis ultrasound images, and incorporating segmentation features to assist the classification task, the developed model achieves high efficiency and lightness.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The proposed model achieved a Dice coefficient of 80.67% and 78.12% for segmentation, and classification accuracy of 90.10% and 80.96% on the internal and external test sets, respectively. Compared to the lightweight state\u2010of\u2010the\u2010art (SOTA) model, it improves inference speed by 43.48% and increases classification accuracy by 8.89% and 16.32% on internal and external test sets, respectively. Compared to the standard SOTA model, it is only 8% of its size but boosts inference speed by 821.37%, with classification accuracy improvements of 3.65% and 4.57% on internal and external test sets, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed model offers efficient and accurate hepatic echinococcosis diagnosis, with a lightweight design suitable for both resource\u2010limited and advanced clinical settings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/hcs2.70077","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T09:34:52Z","timestamp":1779183292000},"page":"284-298","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and External Validation of an Efficient Deep Learning Model for Lesion Segmentation and Subtyping of 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