LIVER CANCER DETECTION USING HYBRIDIZED FULLY CONVOLUTIONAL NEURAL NETWORK BASED ON DEEP LEARNING FRAMEWORK. - SHANMUGAVADIVEL, G.; KUMAR, PNANDHA; KUMAR, V. SNAVEEN; PARTHIBAN, V. (Turkish Journal of Physiotherapy Rehabilitation, 2021)
Liver cancer is one in every of the foremost death factors within the world. Early detection and correct staging of liver disease is a crucial issue in sensible radiology. Liver lesions check with those abnormal tissues that are found within the liver. Liver lesions are a wound or injury within the tissue areas of the body thanks to injury caused by a wound or sickness. These lesions are often known during a CT scan by a distinction in element intensity from different regions of the liver. For correct clinical treatment, physical segmentation of CT scan is tough and a long task. Instead, computerized segmentation may be a terribly difficult task, as well as liver extend by a hundred and fifty slices of a CT image. Segmentation of liver tumors is a crucial necessity task before any surgical intervention. An exact and correct analysis of the lesions allows for the obtainable therapies that may be provided to the patient. It will facilitate decide the simplest treatment approach also as track the progress of the medical aid over associate interval of your time. So as to subsume this challenge, during this project we have a tendency to propose the utilization of advanced machine learning and segmentation techniques named as guided Active Contour(GAC) technique to cluster the lesions. Then using features extraction techniques to extract the options and phase the liver supported similar pixels. And additionally implement Convolutional Neural Network algorithmic rule to classify the liver diseases with improved accuracy rate.