A Deep Learning Approach for Kidney Disease Recognition and Prediction through Image Processing


Chronic kidney disease (CKD) is a gradual decline in renal function that can lead to kidney damage or failure. As the disease progresses, it becomes harder to diagnose. Using routine doctor consultation data to evaluate various stages of CKD could aid in early detection and prompt intervention. To this end, researchers propose a strategy for categorizing CKD using an optimization technique inspired by the learning process. Artificial intelligence has the potential to make many things in the world seem possible, even causing surprise with its capabilities. Some doctors are looking forward to advancements in technology that can scan a patient’s body and analyse their diseases. In this regard, advanced machine learning algorithms have been developed to detect the presence of kidney disease. This research presents a novel deep learning model, which combines a fuzzy deep neural network, for the recognition and prediction of kidney disease. The results show that the proposed model has an accuracy of 99.23%, which is better than existing methods. Furthermore, the accuracy of detecting chronic disease can be confirmed without doctor involvement as future work. Compared to existing information mining classifications, the proposed approach shows improved accuracy in classification, precision, F-measure, and sensitivity metrics.
The manufacturer demonstrated a preference for using iridology to distinguish between different forms of kidney disease, either normal or exceptional. A total of 192 individuals with chronic kidney disease and 169 healthy individuals were evaluated. A method for acquiring, processing, and characterizing iris images using wavelet transformation and a flexible neuro-fuzzy inference system was developed to reduce dependence on iridologists. The results showed, for both individuals with kidney problems and healthy individuals, an accuracy rate of 81% and 92%, respectively. A CNN was constructed to identify 10 major crop diseases using a database of 500 photos of healthy and diseased grain stems and leaves collected from agricultural fields. The proposed convolution neural algorithm achieved an accuracy of 95.48% using a 10-fold cross-validation architecture. This accuracy is significantly greater than a traditional classification model
The training and testing stages 1 to 5 are carried out for the left kidney (98.34%) and right kidney (97.46%) and then the overall accuracy is evaluated (99.23%). The analysis of the existing TRM method shows a kidney stage 1 to 5 training and testing accuracy of 95.76% and an overall accuracy of 97.46%. The results indicate that the HFNN method provides the best performance compared to the existing method.
The results show that the proposed model outperforms the existing method in more accurate disease identification. Due to the small sample size of the dataset used in the research, it has been decided that future work will be carried out with bigger datasets or by comparing the outcomes of this dataset with those of another dataset. Also, in an effort to reduce the prevalence of CKD, an effort has been made to determine whether an individual with this syndrome is more likely to have chronic risk factors such diabetes, hypertension, or a family history of kidney failure.

Kailash Kumar, M. Pradeepa, Miroslav Mahdal, Shikha Verma, M. V. L. N. RajaRao and Janjhyam Venkata Naga Ramesh

College of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi Arabia

School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India

Department of Control Systems and Instrumentation, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava, Czech Republic

Department of Computer Applications, ABES Engineering College, Ghaziabad 201009, India

Department of Information Technology, Seshadri Rao Gudlavalleru Engineering College, Vijayawada 521356, India

Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur 522302, India

Download full abstract: https://iridology-research.com/pdf/applsci-13-03621-v2.pdf