Kidney image classification using transfer learning with convolutional neural network

Abstract

For abdominal studies, one of the most widely used diagnostic methods is ultrasound imaging. Several chronic kidney diseases (CKDs) such as kidney stone, cystic kidney, and hydronephrosis are present in the human kidney. These CKDs, later on, lead to the development of a number of severe diseases particularly heart diseases, pulmonary attacks, cardiomyopathy, etc. Therefore, early detection of CKDs is highly desirable in clinical practices as it can save hundreds of lives. Nowadays, the main focus of researchers is to develop automatic disease detection methods, avoiding the need for human interaction. The study of deep learning models is playing a critical role in various applications of healthcare not only due to their fast and accurate results, but also minimal manual interference is required in these methods. In this paper, two approaches are proposed for the detection of CKDs in ultrasound kidney images. The first one is a conventional approach that uses GA optimised neural network (GAONN) as classifier, whereas in other approach, convolution neural network model such as AlexNet is used for automatic detection of diseases. AlexNet is trained using the transfer learning process. Experimental results show that CNN performs better than GA optimised neural network in classifying kidney images.