A Novel Approaches for Detect Liver Tumor Diagnosis using Convolution Neural Network

Liver cancer is on the rise and may now be the most common form of the disease that claims lives. Superpixel segmentation as well as a convolutional neural network (CNN) technique are used to assess liver cancer detection while cutting down on complexity and processing time. Automatic 3D segmentation of ultrasound liver images using a combination of a time-consuming but accurate technique and a statistical texture before high-energy sound waves. Each image in the area surrounding the database point has texture characteristics extracted using two orthogonal quadratic filter banks. The atlas database has segmented liver surfaces and registered photos of livers from prior patients. The CNN technique is used to first segment the pixel location segmentation of the liver imaging from a new patient, and then to train the challenge of patient-specific frequency components in the feature representation from the atlas database. Liver cancer detection datasets include both benign and malignant cases.