Automated detection of chronic kidney disease using deep learning

Chronic kidney disease (CKD) is recognized as a growing global health concern, affecting millions of individuals worldwide and often progressing to end-stage renal disease (ESRD), necessitating dialysis or kidney transplantation. Early detection of CKD plays a vital role in timely medical intervention and improving patient prognosis. However, traditional diagnostic techniques, including serum creatinine levels, glomerular filtration rate (GFR), and urine albumin tests, may fail to identify the disease at its early stages, emphasizing the need for automated and advanced diagnostic methodologies. This study proposes an image-processing-based and statistical approach for the automated detection of CKD using medical imaging datasets, including ultrasound, CT, and MRI images. Feature extraction techniques are applied to identify distinct patterns within kidney images, enhancing diagnostic accuracy and enabling classification of images into CKD and non-CKD categories. The proposed model achieved an accuracy of 92%, a precision of 89%, recall of 91%, and an F1-score of 90%, surpassing the performance of traditional machine learning classifiers such as Logistic Regression and Support Vector Machines (SVM). Moreover, the Area Under the Curve (AUC-ROC) score of 0.95 further validates the robustness and reliability of the proposed system. Future work aims to integrate additional clinical parameters, such as blood test results and genetic biomarkers, to further refine the diagnostic framework. The implementation of computer-assisted diagnostic systems is expected to enhance clinical decision-making and improve patient care outcomes. In conclusion, the study highlights the potential of image-based diagnostic techniques for early CKD detection, contributing to efficient, accurate, and accessible healthcare solutions.