Detection and classification of medical images using deep learning for chronic kidney disease

Chronic kidney disease (CKD) is an advancing disease which significantly impacts global healthcare, requiring early detection and prompt treatment is required to prevent its advancement to end-stage renal disease. Conventional diagnostic methods tend to be invasive, lengthy, and costly, creating a demand for automated, precise, and efficient solutions. This study proposes a novel technique for identifying and classifying CKD from medical images by utilizing a Convolutional Neural Network based Crow Search (CNN based CS) algorithm. The method employs sophisticated pre-processing techniques, including Z-score standardization, min-max normalization and robust scaling to improve the input data's quality. Selection of features is carried out using the chi-square test, and the Crow Search Algorithm (CSA) further optimizes the feature set for the improvement of accuracy classification and effectivess. The CNN architecture is employed to capture complex patterns using deep learning methods to accurately classify CKD in medical pictures. The model optimized and examined using an open access Kidney CT Scan data set. It achieved 99.05% accuracy, 99.03% Area under the Receiver Operating Characteristic Curve (AUC-ROC), and 99.01% Area under the precision-recall curve (PR-AUC), along with high precision (99.04%), recall (99.02%), and F1-score (99.00%). The results show that the CNN-based CS method delivers high accuracy and improved diagnostic precision related to conventional machine learning techniques. By incorporating CSA for feature optimization, the approach minimizes redundancy and improves model interpretability. This makes it a promising tool for automated CKD diagnosis, contributing to the development of AI-driven medical diagnostics and providing a scalable solution for early detection and management of CKD.