Automated Detection of Chronic Kidney Disease (CKD) using Deep Learning

A recognized increasing worldwide health issue, chronic kidney disease (CKD) affects millions of people and usually advances to end-stage renal disease (ESRD), which calls for dialysis or kidney transplant. Timely medical management and patient prognosis enhancement depend much on early CKD detection. Emphasizing the requirement of automated and improved diagnostic methods, nonetheless, conventional diagnostic methods such serum creatinine levels, glomerular filtration rate (GFR), and urine albumin tests could miss the disease at its early stages. By combining image processing and statistical methods utilizing ultrasound and CT and MRI medical images, the study suggests a strategy to automatically identify CKD. Feature extraction techniques enable medical practitioners to identify certain patterns in kidney images that enhance diagnosis accuracy as well as non-CKD and CKD image categorization. The proposed model surpassed traditional machine learning classifiers such as Logistic Regression and Support Vector Machines (SVM) by means of its achieved 90% F1-score, 92% accuracy, 91% recall, and 89% precision levels. Its AUC-ROC score of 0.95 helps to further confirm the proposed system’s reliability and robustness. Planned future work will see genetic biomarkers and extra blood test findings included into the diagnosis system. Computerized medical diagnosis tools will support physician choices and improve patient outcomes during medical treatment. The research shows how picture-based diagnostic techniques might identify CKD early, hence supporting effective, precise, and easily available healthcare options..