Journal of Advances in Developmental Research

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Federated Reinforcement Learning Assistance IOT Consumers for Kidney Image Diseases

Author(s) G. Bhargavi, R. Madhu Sekhar, S. Apsaleha, M. Akhila, T. Leelavathi
Country India
Abstract Ongoing kidney infection, much of the time called steady kidney dissatisfaction, is a reliable rot of capacity for kidneys. Growths, stones, and cancers are probably the most common reasons for kidney disappointment. In there may be no symptoms during the early stages of chronic renal disease. Then again, kidney sickness might go undiscovered until it is past the point of no return. Fortunately, unique cerebrum networks have been shown to be profitable in early sickness assumption as simulated intelligence and programming have advanced. We have divided kidney CT images into four categories: normal, cyst, stone, and growth — utilizing three CNN grouping techniques that depend on watershed division and make utilization of profound brain organizations (DNN). Our work includes two stages. We have first separated the district of decision in CT images as calculated by the watershed from that point onward, the divided kidney information was utilized in the process of training a number of classification networks, one of which was EAnet, a transfer learning-based pre-prepared brain network called ResNet50, as well as a tweaked CNN model. The CT Kidney Ordinary Growth Cancer and Stone dataset, which was made accessible on Kaggle, was utilized to prepare the models. EANet, ResNet50, and the proposed CNN model all performed well on the test set of classification models. accomplished 83.65%, 87.92%, and 98.66% precision, individually. We saw that the proposed CNN model had the best in general exactness as well as the most elevated responsiveness and particularity.
Keywords Chronic Kidney Disease, Convolutional Neural Network (CNN), EAnet, Resnet50
Field Engineering
Published In Volume 17, Issue 1, January-June 2026
Published On 2026-04-04
Cite This Federated Reinforcement Learning Assistance IOT Consumers for Kidney Image Diseases - G. Bhargavi, R. Madhu Sekhar, S. Apsaleha, M. Akhila, T. Leelavathi - IJAIDR Volume 17, Issue 1, January-June 2026.
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