A novel method for predicting kidney diseases using optimal artificial neural network in ultrasound images. (24th January 2020)

• Record Type: Journal Article
• Authors: Balamurugan, S. Packirisamy; Arumugam, Gurusamy
• Is Part Of: International journal of intelligent enterprise. Volume 7:Number 1/3(2020)
• Page Start: 37
• Page End: 55
• Publication Date: 2020-01-24
• Subjects: ultrasound image -- neural network -- multi-kernel k-means clustering -- GLCM features -- segmentation -- classification -- bilateral filter -- oppositional grasshopper optimisation algorithm -- OGOA; Organizational learning -- Periodicals; Knowledge management -- Periodicals; Business intelligence -- Periodicals; Business enterprises -- Design -- Periodicals; Organizational effectiveness -- Periodicals; 658.005
• Languages: English
• ISSNs: 1745-3232

Abstract

The main aim of this research is to design and develop an efficient approach for predicting ultrasound kidney diseases using multiple stages. Nowadays, kidney disease prediction is one of the crucial procedures in surgical and treatment planning for ultrasound images. Therefore, in this paper, we propose a novel ultrasound kidney diseases prediction using the artificial neural network (ANN). To achieve the concept, we comprise the proposed system into four modules such as preprocessing, feature extraction, feature selection using OGOA and disease prediction using ANN. Initially, we eliminate the noise present in the input image using the optimal wavelet and bilateral filter. Then, a set of GLCM features are extracted from each input image and then we select the important features using oppositional grasshopper optimisation algorithm (OGOA). To classify the image as normal or abnormal, the proposed method utilises an artificial neural network (ANN). The performance of the proposed method is evaluated using accuracy, sensitivity, and specificity. The experimentation results show that the proposed system attains the maximum accuracy of 95.83% which is high compared to existing methods.