Computer-Aided Diagnosis System for Classifying the Liver Lesions from Multiphase CT Images

Background and Objective: Liver cancer is one of the lethal cancers, with a high mortality rate. This study aims to develop a Computer-Aided Diagnosis (CAD_x) system for identifying benign, malignant and metastatic tumors using multi-phase 3D Computed Tomography (CT) data. Materials and Methods: The proposed study uses 601 retrospective cases from an internal institutional database consisting of benign (n = 208), malignant (n = 200) and metastases (n = 193) and 105 Hepatocellular Carcinoma (HCC) cases from a public dataset. The liver is segmented automatically using a Deep Learning (DL) model based on SegNet and atrous spatial pyramid pooling module. Features are extracted from the segmented liver volume using histogram, texture, wavelet and DL methods for characterizing the three categories. The relevant features are then fed to the standard classifiers for comparative analyses. Results: The proposed DL-based liver segmentation method performed better than the standard DL methods. Support vector machine gave the best results for both test sets among the classifiers. The average classification accuracies achieved were 80 and 81.9% for the internal and public datasets. Conclusion: The proposed CAD_x system has good clinical potential in distinguishing liver lesions from multi-phase CT images. The promising results obtained for internal and public datasets prove the model’s generalizability.