Renal Vision Advanced Kidney Disease Detection Using Attention-Powered Ensemble CNNs

The kidneys eliminate waste, pollutants, and unnecessary water from the bloodstream, which helps to sustain general health. Impaired kidney functioning can have solemn effects for someone's health. Machine learning (ML) approaches have revealed to be operative tools for enlightening clinical decision-making and reducing ambiguity. However, current approaches for detecting kidney disease are frequently imprecise because to biological characteristic constraints. This study delivers a progressive machine learning model that forecasts renal illness by combining preprocessing procedures, hyper parameter optimization, feature selection and Machine Learning algorithms. To improve detection accuracy, a Convolutional Neural Network (CNN) is used in aggregation with an attention mechanism. The model identifies kidney anomalies, for example cysts, stones, and cancers, as markers of renal illness. The model was validated using k-fold cross-validation, and the dataset contained around 4000 photos (1000 in each class). The suggested CNN with concentration model has a remarkable accuracy of 99.87% in diagnosing various kidney disease kinds. This version simplifies the language and simplifies the process while leaving the important elements intact.