  The shortage of nephrologists and the growing public health concern over
renal failure have spurred the demand for AI systems capable of autonomously
detecting kidney abnormalities. Renal failure, marked by a gradual decline in
kidney function, can result from factors like cysts, stones, and tumors.
Chronic kidney disease may go unnoticed initially, leading to untreated cases
until they reach an advanced stage. The dataset, comprising 12,427 images from
multiple hospitals in Dhaka, was categorized into four groups: cyst, tumor,
stone, and normal. Our methodology aims to enhance CT scan image quality using
Cropping, Resizing, and CALHE techniques, followed by feature extraction with
our proposed Adaptive Local Binary Pattern (A-LBP) feature extraction method
compared with the state-of-the-art local binary pattern (LBP) method. Our
proposed features fed into classifiers such as Random Forest, Decision Tree,
Naive Bayes, K-Nearest Neighbor, and SVM. We explored an ensemble model with
soft voting to get a more robust model for our task. We got the highest of more
than 99% in accuracy using our feature descriptor and ensembling five
classifiers (Random Forest, Decision Tree, Naive Bayes, K-Nearest Neighbor,
Support Vector Machine) with the soft voting method.
