Efficient Computer Aided Diagnosis System for Hepatic Tumors Using Computed Tomography Scans

Abstract

One of the leading causes of mortality worldwide is liver cancer. The earlier the detection of hepatic tumors, the lower the mortality rate. This paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign tumors. Segmenting hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range, intensity values overlap between the liver and neighboring organs, high noise from computed tomography scanner, and large variance in tumors shapes. The proposed method consists of three main stages; liver segmentation using Fast Generalized Fuzzy C-Means, tumor segmentation using dynamic thresholding, and the tumor's classification into malignant/benign using support vector machines classifier. The performance of the proposed system was evaluated using three liver benchmark datasets, which are MICCAI-Sliver07, LiTS17, and 3Dircadb. The proposed computer adided diagnosis system achieved an average accuracy of 96.75%, sensetivity of 96.38%, specificity of 95.20% and Dice similarity coefficient of 95.13%.

MicroRNA Biosensors for Early Detection of Hepatocellular Carcinoma

“…Therefore, early diagnosis and detection of hepatocellular carcinoma is crucial to improve patient prognosis and survival. The main clinical methods for detecting hepatocellular carcinoma include computed tomography (CT) [3], ultrasonography (US) [4], magnetic resonance imaging (MRI) [5], and tissue biopsy [6]. These methods have limitations in detection sensitivity, expensive detection equipment, and are heavily operator-dependent and invasive to the patient's body [7].…”

Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism

“…In the work of Yasmeen Al-Saeed, a computer-aided diagnosis system was introduced to extract liver tumors from computed tomography scans and classify them as malignant or benign. The proposed computer aided diagnosis system achieved an average accuracy of 96.75%, sensitivity of 96.38%, specificity of 95.20% and Dice similarity coefficient of 95.13% [ 37 ]. However, this study still has shortcomings such as a single sample type.…”