Automated Kidney Abnormality Detection Using CT Images

Abstract

Kidney abnormalities, including conditions such as kidney stones, cysts, tumors, and chronic kidney disease, pose significant health risks and require early and accurate diagnosis for effective treatment. Traditional diagnostic methods, such as imaging analysis and laboratory tests, are often time-consuming and depend heavily on expert interpretation. To address these challenges, this study proposes a machine learning (ML)- based approach for the detection of kidney abnormalities using medical data and imaging features. The proposed system utilizes various machine learning algorithms such as Support Vector Machines (SVM), Random Forest, and Logistic Regression to analyze clinical and imaging data. The process involves data preprocessing, feature extraction, and model training using labeled datasets. Advanced techniques are applied to handle noise, missing values, and class imbalance to improve model performance. The trained models are evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable detection. Experimental results demonstrate that machine learning models can effectively identify kidney abnormalities with high accuracy and efficiency. The system provides a cost-effective, scalable, and automated solution that assists medical professionals in early diagnosis and decision-making. Overall, the proposed approach enhances the reliability of kidney disease detection and contributes to improved patient outcomes.