An Optimal Modified Faster Region CNN Model for Diagnosis of Liver Diseases from Ultrasound Images

Abstract

In recent years, the rate of mortality and morbidity is increased due to uncontrolled conditions of liver diseases. Liver disorders are mainly caused because of inhalation of toxic gases, consumption of contaminated food, alcohol usage, etc. If the progression of liver disease is not detected at an early stage, it seriously threatens human life. Early diagnosis and timely treatment are the only ways to prevent such life-threatening diseases. This paper mainly aims to design an automated prediction model using ultrasound liver images. Due to non-invasive, cost-effective, and real-time imaging abilities, the ultrasound becomes most preferable modality. Therefore, the main purpose of the paper is at predicting liver diseases using the proposed modified faster region-based convolutional neural network-based hybrid grasshopper tunicate swarm (MFRCNN-HGTS) approach. The distortions in the raw ultrasound liver images are removed and the number of training images is increased by the augmentation process. The augmented data samples are then allowed to pass through a modified faster region-based convolutional neural network (MFRCNN) classifier to predict normal and abnormal instances from the images. To improve classification accuracy, the weight factors are assigned with appropriate values using the hybrid grasshopper tunicate swarm (HGTS) algorithm. The proposed MFRCNN-HGTS approach predicts liver diseases accurately and classifies them based on texture features as normal, cirrhosis, or hepatitis. The experimental setup is performed using MATLAB R2016a software. The proposed MFRCNN-HGTS method is evaluated by comparing it with other traditional methods. The analytic result illustrates that the proposed MFRCNN-HGTS techniques achieve 98.45% accuracy over other techniques.