Open Access

ARTICLE

Liver-Tumor Detection Using CNN ResUNet

Muhammad Sohaib Aslam¹, Muhammad Younas¹, Muhammad Umar Sarwar¹, Muhammad Arif Shah^2,*, Atif Khan³, M. Irfan Uddin⁴, Shafiq Ahmad⁵, Muhammad Firdausi⁵, Mazen Zaindin⁶

1 Department of Computer Science, Government College University Faisalabad, Faisalabad, 38000, Pakistan
2 Department of IT and Computer Science, Pak-Austria Fachhochschule Institute of Applied Sciences and Technology, Haripur, 22620, Pakistan
3 Department of Computer Science, Islamia College Peshawar, Peshawar, KP, Pakistan
4 Institute of Computing, Kohat University of Science and Technology, Kohat, Pakistan
5 Department of Industrial Engineering, College of Engineering, King Saud University, Riyadh, 11421, Saudi Arabia
6 Department of Statistics and Operations Research, College of Science, King Saud University, Riyadh, 11421, Saudi Arabia

* Corresponding Author: Muhammad Arif Shah. Email:

(This article belongs to the Special Issue: Deep Learning and Parallel Computing for Intelligent and Efficient IoT)

Computers, Materials & Continua 2021, 67(2), 1899-1914. https://doi.org/10.32604/cmc.2021.015151

Received 08 November 2020; Accepted 05 December 2020; Issue published 05 February 2021

Abstract

Liver tumor is the fifth most occurring type of tumor in men and the ninth most occurring type of tumor in women according to recent reports of Global cancer statistics 2018. There are several imaging tests like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound that can diagnose the liver tumor after taking the sample from the tissue of the liver. These tests are costly and time-consuming. This paper proposed that image processing through deep learning Convolutional Neural Network (CNNs) ResUNet model that can be helpful for the early diagnose of tumor instead of conventional methods. The existing studies have mainly used the two Cascaded CNNs for liver segmentation and evaluation of Region Of Interest (ROI). This study uses ResUNet, an updated version of U-Net and ResNet Models that utilize the service of Residential blocks. We apply over method on the 3D-IRCADb01 dataset that is based on CT slices of liver tumor affected patients. The results showed the True Value Accuracy around 99% and F1 score performance around 95%. This method will be helpful for early and accurate diagnose of the Liver tumor to save the lives of many patients in the field of Biotechnology.

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