Examining feature extraction and classification modules in machine learning for diagnosis of low-dose computed tomographic screening-detected lesions.

Purpose: Medical imaging-based machine learning (ML) for computer-aided diagnosis of lesions consists of two basic components or modules of (i) feature extraction from non-invasively acquired medical images and (ii) feature classification for prediction of malignancy of lesions detected or localized in the medical images. This study investigates their individual performances for diagnosis of low-dose computed tomography (CT) screening-detected lesions of pulmonary nodules and colorectal polyps.

Approach: Three feature extraction methods were investigated.