• Nikhat khan
• Prakash Mohod

The liver is the second organ most generic involved by metastatic disease being liver cancer one of the prominent causes of death worldwide. Without healthy liver a person cannot survive. It is life threatening disease which is very challenging distinct for both medical and engineering technologists. Medical Science is growing field with the parallel working of information technology and improvement of automated system instruments. Disease prediction can be done using evaluation performance over medical data with many different approaches which can process the data. Liver cancer is one of the disease which cause by many regular or irregular activity. Liver cancer prediction from the reports may needed proper human skillset and on time skillset to find analysed data. The machine learning mining algorithm can work towards to analyse the disease from the medical report feature and help in prediction. Medical image processing is used as a non-invasive method to reveal tumours. The chances of survival having liver Tumor highly depends on early detection of Tumor and then classification as cancerous and non-cancerous tumours. With the help of Image processing techniques containing pre-processing and enhancement, image segmentation, classification have been developed for the detection of liver Tumor and different liver tumor detection algorithms and methodologies utilized for Tumor diagnosis.

CT Image, Computed Tomography, Segmentation, Image Processing and Enhancement, Cancerous, Non-cancerous, Classification.

"A REVIEW ON: IMAGE PROCESSING BASED LIVER CANCER DETECTION METHOD", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.662-666, June 2019, Available :http://www.jetir.org/papers/JETIR1908893.pdf

"A REVIEW ON: IMAGE PROCESSING BASED LIVER CANCER DETECTION METHOD", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp662-666, June 2019, Available at : http://www.jetir.org/papers/JETIR1908893.pdf