Title: Enhanced Liver Tumor Diagnosis Using Data Mining and Computed Tomography (CT)

Year of Publication: Apr - 2014
Page Numbers: 254-261
Authors: Ayman E. Khedr, Awad Khalil and Mohammed A. Osman
Conference Name: The International Conference on Computing Technology and Information Management (ICCTIM2014)
- United Arab Emirates

Abstract:


The main objective of this study is to provide a Computer-Aided Diagnosis (CAD) system for the diagnosis process of benign and malignant liver tumors from computed tomography (CT). Also it aimed to evaluate the potential role of Fuzzy Clustering Means (FCM) and neural network in the differential diagnosis processes of liver tumors in CT images. In this study, liver tumors are classified as hepatocellular carcinoma (cancer) and hemangioma (benign). By using FCM each suspicious tumor region is automatically extracted from liver images. Consequently, textural features are obtained. These features are used to train the Neural Network (NN) and classify the tumors. The system distinguishes tumors with high accuracy and is therefore clinically useful.