Classification of Nonalcoholic Fatty Liver Texture Using Convolution Neural Networks

Fatty liver disease is the major cause for the liver dysfunction and is highly prevalent in developed and developing nations. Fatty liver can progress into chronic diseases if left untreated. Depending on the density of accumulated fat in liver, the liver is diagnosed into three classes/grades namely Grade 1, Grade 2, and Grade 3 apart from being Normal. For fatty liver diagnosis, doctors use the texture properties of liver parenchyma to quantify the fat in liver. The texture properties of ultrasonic liver parenchyma changes with the proportion of fat and acts as a useful feature for the doctors in doing the diagnosis. The diagnostic accuracy of fatty liver is less due to minute variations observed in the texture properties. Hence to improve the classification accuracy in diagnosing the fatty liver, we propose a convolution neural network based computer-aided diagnosis algorithm for categorizing the ultrasound liver parenchyma texture into four classes. The proposed algorithm is analyzed using 1000 texture images comprising of 250 images belonging to each class. Performance analysis shows that the proposed framework classifies the texture with an accuracy of 93.5% when 80% and 20% of data used for training and testing respectively.