Kidney Anomalies, Including Cysts, Tumors, And Structural Abnormalities, Are Among The Leading Causes Of Renal Dysfunction And Can Progress To Severe Chronic Kidney Disease If Not Diagnosed Early. Traditional Diagnostic Methods, Such As Ultrasound And CT Scan Interpretation, Rely Heavily On Radiologists’ Expertise, Making Them Time-consuming, Subjective, And Prone To Human Error. With Recent Advancements In Artificial Intelligence, Deep Learning (DL) Has Emerged As A Powerful Tool For Automated Medical Image Analysis, Offering High Accuracy In Detecting Subtle Pathological Changes. This Study Proposes An Intelligent Framework For Automated Kidney Anomaly Detection Using Deep Learning Models Integrated With Explainable AI (XAI) Techniques. The DL Models Are Trained On Medical Imaging Datasets To Identify And Classify Kidney Anomalies With Improved Precision, While XAI Methods, Such As Grad-CAM And SHAP, Provide Visual And Interpretable Explanations Of Model Decisions. This Combination Not Only Enhances The Transparency And Reliability Of AI-driven Diagnostics But Also Assists Clinicians In Making Informed Decisions. The Proposed System Demonstrates Significant Potential In Early Detection, Reduced Diagnostic Workload, And Improved Patient Outcomes, Paving The Way For Trustworthy And Clinically Applicable AI Solutions In Nephrology.
.jpg "AUTOMATED KIDNEY ANOMALY DETECTION USING DEEP LEARNING AND EXPLAINABLE AI TECHNIQUES")