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.

翻译：肾脏科医生短缺以及肾衰竭日益成为公共卫生关注焦点，催生了对能够自主检测肾脏异常的人工智能系统的需求。肾衰竭以肾功能逐渐下降为特征，可能由囊肿、结石和肿瘤等因素引发。慢性肾脏病在初期可能被忽视，导致病例在发展到晚期前未得到治疗。数据集包含来自达卡多家医院的12,427张图像，分为四类：囊肿、肿瘤、结石和正常。我们的方法旨在通过裁剪、调整大小和CALHE技术提升CT扫描图像质量，随后使用我们提出的自适应局部二值模式（A-LBP）特征提取方法（与现有最先进的局部二值模式（LBP）方法对比）进行特征提取。将所提出的特征输入随机森林、决策树、朴素贝叶斯、K近邻和支持向量机等分类器。我们探索了采用软投票的集成模型，以构建更稳健的任务模型。使用我们的特征描述符并结合五种分类器（随机森林、决策树、朴素贝叶斯、K近邻、支持向量机）的软投票集成方法，我们获得了超过99%的最高准确率。