We are developing a computerized method that detects suspicious areas on ultrasound images, and then distinguishes between malignant and benign-type lesions. The computerized scheme identifies potential lesions based on expected lesion shape and margin characteristics. All potential lesions are subsequently classified by a Bayesian neural net based on computer-extracted lesion features. The scheme was trained on a database of 400 cases (757 images) - consisting of complex cysts, benign and malignant lesions - and tested on a comparable database of 458 cases (1740 images) including 578 normal images. We investigated the performances of lesion detection and subsequent classification by a Bayesian neural net for two tasks. The first task was the distinction between actual lesions and false-positive (FP) detections, and the second task the distinction between actual malignant lesions and all detected lesion candidates. In training, the detection and classification method obtained an A_z value of 0.94 in the distinction of false-positive detections from actual lesions, and an A_z of 0.91 was obtained on the testing database. The task of distinguishing malignant lesions from all other detections (false-positives plus all benign type lesions) showed to be more challenging and A_z values of 0.87 and 0.81 were obtained during training and testing, respectively. For the testing database, the combined detection and classification scheme correctly identified lesions in 82% (0.45 FP per image) of all the patients, and in 100% (0.43 FP malignancies per image) of the cancer patients.
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