Liver cancer contributes to the increasing mortality rate in the world. Therefore, early detection may lead to a decrease in morbidity and increase the chance of survival rate. This research offers a computer-aided diagnosis system, which uses computed tomography scans to categorize hepatic tumors as benign or malignant. The 3D segmented liver from the LiTS17 dataset is passed through a Convolutional Neural Network (CNN) to detect and classify the existing tumors as benign or malignant. In this work, we propose a novel light CNN with eight layers and just one conventional layer to classify the segmented liver. This proposed model is utilized in two different tracks; the first track uses deep learning classification and achieves a 95.6% accuracy. Meanwhile, the second track uses the automatically extracted features together with a Support Vector Machine (SVM) classifier and achieves 100% accuracy. The proposed network is light, fast, reliable, and accurate. It can be exploited by an oncological specialist, which will make the diagnosis a simple task. Furthermore, the proposed network achieves high accuracy without the curation of images, which will reduce time and cost.
