Focal Liver Lesion Classification Based on Receiver Operating Characteristic Analysis

Background: Computer-Aided Diagnosis (CAD) on Focal Liver Lesion (FLL) has been widely researched. It aims at classifying liver images into malignant or benign, so as to help doctors to make corresponding diagnosis. In most existing CAD systems, the automatic decision strategies on challenging cases usually lead to risky diagnosis. Objective: In this paper, we adopted a ROC optimal abstention model for FLL classification to reduce the misclassification risk. Method: The workflow of ROC based FLL classification includes the stages of feature extraction, statistic for building ROC curve and ROC optimal abstaining classification. Through investigating the properties of ROC, we can automatically find two optimal thresholds for building the abstention model. A part of cases refrains from being classified to achieve the lowest misclassification cost. Results: The model classifies the FLL medical records into positive (malignant), negative (benign) and abstaining cases. The abstained challenging cases can be carefully examined by experts in order to reduce the misclassification risk. Conclusion: Abundant experiments indicate that the proposed method can achieve satisfied results and is effective for FLL diagnosis.