Convolutional Neural network-based classifiers for liver tumor detection using Computed Tomography scans

Abstract

Automatic detection and segmentation of liver tumors from CT scans is a challenging task due to their complex morphology in CT images, but advances in deep learning have made it possible to achieve high accuracy in the segmentation and detection of liver tumors, eliminating the need for manual intervention and allowing radiologists to focus more on critical diseases by giving awareness about the liver tumor at an early stage, which may save a person's life by allowing them to receive treatment sooner, possibly preventing further severity or death. In our study, we worked on different approaches to detect liver tumors from CT scans and analyzed their performance in terms of accuracy. A few of the approaches employed are using Convolutional neural networks including existing ones like U-Net, ResNet50, while we proposed a combination of multiple CNN models to obtain better results. It was observed that the combination of CNN models produced higher accuracies rather than the existing models.