INTRAHEPATIC CHOLANGIOCARCINOMA DETECTION AND SEGMENTATION OF IMAGES USING DEEP LEARNING TECHNIQUES

Abstract

Intrahepatic cholangiocarcinoma is a form of cancer that forms in the cells of the bile ducts, both inside and outside of the liver. Cholangiocarcinoma and bile duct cancer are two words that are often used interchangeably to describe the same disease. Therefore, we have proposed an intelligent Hepatoma detection system. So, the main purpose of this research is to develop and implement an automated method that will help to detect and classify the Liver Cancer disease by processing hepatomic images. We have used liver-tumor-segmentation dataset for the testing our proposed methodology, it contains 130 images of Liver Cancer patients. We have applied pre-processing techniques on these images such as morphological filtering, in order to enhance images from input data for post processing. After obtaining the resultant image we have applied slicing. We have used UNets (modified form of convolutional Neural Network) for classification purpose with ResNet34, 50 and 100 architecture for downsampling and upsampling of shifted pixels. The proposed technique provides a sophisticated diagnosis and classification accuracy when compared with previous techniques. The parameter we used to validate the performance of our proposed technique is Top-N accuracy. Our proposed method shows the accuracy of about 99.8%.