Liver Image Classification Using Exhaustive Classifier and Nature Inspired Algorithms

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Senthilkumar Ramachandran
Thirupathi Regula


A significant internal organ of the human body is the liver. Its functions include the detoxification of poisonous compounds, creation of bile and albumin, the synthesis of lipids, carbohydrates, and proteins, as well as many other kinds of chemical elements used in daily life for food digestion. Hepatitis is one of the diseases that affect the human liver nowadays as a result of viral infections. Alcoholism causes fatty liver and cirrhosis illnesses, and medicines, chemicals, and pest control items in dietary goods induce liver problems. Hemochromatosis, Wilson disease, and liver tumors or cancer impact a significant number of individuals. Chronic liver conditions include cirrhosis, fibrosis, and chronic hepatitis. To detect its harms and for curing process, liver disorders are identified utilizing the widely used techniques such as tissue biopsies, blood tests, ultrasound effects, CT scans, and MRI.Paper gives a summary of the contribution of the researchers over the preceding five years to identify liver illness using medical images of the affected liver, artificial neural network algorithms. The goal of our proposed investigation approach is to distinguish between abnormal and normal liver, using six classifying methodologies to determine whether the liver is healthy or affected by diseases using medical images of the people. Nature inspired algorithms to do the feature selection task in our model as well avoid the unnecessary features which pulling down the performance. Methodologies includes SVM, eXtremeBoost, ANN, NB,CNN and LR. The said methods will be arranged with three unique algorithms in a group out of six, likewise it will be grouped the rest of them. The final results of exhaustive classifiers of each method's performance in their group will be compared with other combination of methods with their results from which the best performed combination will be identified to detect the liver issues and provide the additional treatment recommendations will then be made, which will bemore useful to doctors and individuals globally in themodern era, who are all affected by liver disease.

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Senthilkumar Ramachandran, & Thirupathi Regula. (2023). Liver Image Classification Using Exhaustive Classifier and Nature Inspired Algorithms. Journal of Coastal Life Medicine, 11(2), 308–318. Retrieved from


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