Abstract

Fatty liver disease is one of those health problems which, if not taken seriously, might lead to the most menacing complications. Henceforth, the diagnosing phase has to be both accurate and just-in-time to allow proper treatment. The approach based on the usage of the convolutional neural network AlexNet for feature extraction of a complex nature and decision-making by the AdaBoost classifier is hence introduced. Extremely high capability of feature extraction by AlexNet made its implementation reach a detection accuracy of 88.5%, in general 5% better than earlier practices. Results have proven that this approach can extract unique features from ultrasound images, which would be useful in diagnosing and managing fatty liver diseases. This could be instrumental in helping the physicians to be right on the spot in the accuracy and quality of diagnosis.