Machine learning based diagnostics of veterinary cancer on ultrasound and optical imaging data.

ABSTRACT: Study advances current diagnostic efficiency of canine/feline (sub-)cutaneous tumors using machine learning and multimodal imaging data. White light (WL), fluorescence (FL) and ultrasound (US) imaging were combined into hybrid approaches to differentiate between malignant mastocytomas, soft tissue sarcomas and benign lipomas. Support Vector Machine and Ensemble classifiers were optimized via sequential feature selection. US radio-frequency signals were quantitatively analyzed to derive the colormaps of six US estimates, corresponding to spectral and temporal domains of the acoustic field. This resulted in the quantification of 72 morphological features for US; as well as 24 and 12 - for WL and FL data, respectively. Resulting classification efficiency for mastocytoma and sarcoma usi

SUBMITTER: Maciulevicius M 

PROVIDER: S-EPMC12493615 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature