• A JEMILA RANI

The early stage liver disease prediction is a significant piece of health-related study, and with this type of research, one may forecast diseases and apply treatments with ease. The numerous types of liver illnesses include cirrhosis, hepatitis, fatty liver, liver cancer, and liver tumours. A critical step in the diagnosis and treatment of liver cancer is the categorization of liver tumours from computed tomography (CT) images. This research presents a convolutional neural network-based automatic approach for segmenting lesions from CT images. The CNNs is one of the deep learning models that uses convolutional filters to learn hierarchical features from data. We contrasted the DNNs model with well-known machine learning methods, Random Forests (RF) and support vector machines (SVM). Using leave-one-out cross validation, experimental evaluation was done on 30 portal phase enhanced CT images. The outcomes demonstrate that the CNNs method is promising for segmenting liver tumours and performs better than other methods.

Computed Tomography, Liver tumours, Random forest algorithm, DNN

"CLASSIFICATION OF LIVER TUMOURS RANDOM FOREST ALGORITHM AND DNN METHOD", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.2, Issue 10, page no.75-82, October-2015, Available :http://www.jetir.org/papers/JETIR1701A06.pdf

"CLASSIFICATION OF LIVER TUMOURS RANDOM FOREST ALGORITHM AND DNN METHOD", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.2, Issue 10, page no. pp75-82, October-2015, Available at : http://www.jetir.org/papers/JETIR1701A06.pdf