Abstract
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.
Abstract (translated)
肾衰竭（肾衰竭）引起的肾小管短缺和公共卫生担忧的增加，促使人们对能够自主检测肾脏异常的AI系统产生需求。肾衰竭可能由囊肿、结石和肿瘤等因素引起。慢性肾衰竭可能最初被忽视，导致直到达到晚期才得到治疗。这个数据集包括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、缩放和局部二值化（CALHE）技术提高CT扫描图像质量，然后使用我们提出的自适应局部二值化（A-LBP）特征提取方法与最先进的局部二值化（LBP）方法进行特征提取。我们将提出的特征输入类学习器，如随机森林（Random Forest）、决策树（Decision Tree）、朴素贝叶斯（Naive Bayes）、K近邻（K-Nearest Neighbor）和支持向量机（SVM）。我们探讨了软投票的集成模型，以获得我们任务的更稳健的模型。我们使用软投票方法获得了超过99%的准确率。
URL
https://arxiv.org/abs/2404.14560
