Liver segmentation and classification in computed tomography images using convolutional neural network and comparison of accuracy with support vector machine

The goal of this research is to assessthe presentation of convolutional Neural Network (CNN) and SVM classifiers in the new liver segmentation categorization using CT images. The CNN and Support Vector Machine (SVM) classifiers are used to recognise the liver CT image collection. Twenty samples were collected and separated into two groups for this study. For ten samples, group 1 used CNN, while group 2 employed SVM with a Gpower of 0.8 for ten samples. CNN produces a credit rate of 96% accuracy, whereas SVM attains a correctness of 87.0%, according to the MATLAB simulation findings. A significant result of P 0.05 wasachieved in statistical analysis. When it came to creative categorization of liver segmentation of the datasets tested, the CNN algorithm outperformed the SVM method in present investigation.