Liver tumour detection and classification using partial differential technique algorithm with enhanced convolutional classifier

• Published in: Journal of intelligent & fuzzy systems Vol. 45; no. 5; pp. 7939 - 7955
• Main Authors: Sasirekha, N., Poonguzhali, I., Shekhar, Himanshu, Vimalnath, S.
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 04.11.2023; Sage Publications Ltd
• Subjects: Algorithms; Boundaries; Classification; Classifiers; Computed tomography; Diagnosis; Gallbladder; Image enhancement; Image segmentation; Intestine; Liver; Medical imaging; Organs; Soft tissues; Tumors; improved convolutional classifier; Liver cancer; level set methodology; accuracy; partial differential technique
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-232218

The image of liver which is the area of interest in this work is obtained from abdominal CT scan. It also contains details of other abdominal organs such as pancreas, spleen, stomach, gall bladder, intestine etc. Since all these organs are of soft tissues, the pixel intensity values differ marginally in the CT scan output and the organs overlap each other at their boundaries. Hence it is very difficult to trace out the exact contour of liver and liver tumor. The overlapping and obscure boundaries are to be avoided for proper diagnosis. Image segmentation process helps to meet this requirement. The normal perception of the CT image can be improved by suitable segmentation techniques. This will help the physician to extract more information from the image and give an accurate diagnosis and better treatment. The projected images are processed using the Partial Differential Technique (PDT) to isolate the liver from the other organs. The Level Set Methodology (LSM) is then used to separate the cancerous tissue from the healthy tissue around it. The classification of stages may be done with the assistance of an Enhanced Convolutional Classifier. The classification of LSM is evaluated by producing many metrics of accuracy, sensitivity, and specificity using an Improved Convolutional classifier. Compared to the two current algorithms, the proposed technique has a sensitivity and specificity of 96% and 93%, respectively, with 95% confidence intervals of [0.7513 1.0000] and [0.7126 1.0000] for sensitivity, and specificity respectively.