Liver Cancer Detection and Grading using Efficient Computer Vision Techniques

Abstract

Early prediction of any kind of cancer always advantageous for on-time medical treatment to save the patient lives. The Computer Aided Diagnosis (CAD) tools using signal processing and image processing methods gained significant attentions for immediate and accurate diagnosis using patient’s raw medical data like Magnetic Resonance Imaging (MRI), Chromatography (CT), etc. The liver cancer early detection and analysis of its grading is important research problem. This paper proposed the CAD system for early detection liver cancer accurately followed by its grading analysis into different stages like stage 1 (T1), stage 2 (T2), and stage 3 (T3). The proposed framework consists of stages like pre-processing, Region of Interest (ROI) extraction, features extraction, and classification. The raw CT scans of liver pre-processed to remove the noises using the filtering and contrast adjustment functions. The adaptive segmentation method designed to using binarization and morphological operations to extract the accurate ROI with minimum computational burden. For features extraction, the text and shape features extracted using Gray Level Co-occurrence Matrix (GLCM) and geometric moment methods respectively. The conventional classifiers such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) applied for prediction. The experimental results shows accuracy of proposed model improved existing methods.