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.

我们的研究旨在利用 CT 扫描图像质量增强方法和自适应局部二进制模式（A-LBP）特征提取方法，结合五个分类器和软投票方法，实现对肾脏异常的自动检测，并取得 99% 以上的准确率。

自适应局部二值模式：基于集成机器学习方法的增强 CT 扫描图像肾脏异常分析的新型特征描述符

Adaptive Local Binary Pattern: A Novel Feature Descriptor for Enhanced  Analysis of Kidney Abnormalities in CT Scan Images using ensemble based  Machine Learning Approach

In this study, we introduce a deep learning approach for segmenting kidney
parenchyma and kidney abnormalities to support clinicians in identifying and
quantifying renal abnormalities such as cysts, lesions, masses, metastases, and
primary tumors. Our end-to-end segmentation method was trained on 215
contrast-enhanced thoracic-abdominal CT scans, with half of these scans
containing one or more abnormalities.
We began by implementing our own version of the original 3D U-Net network and
incorporated four additional components: an end-to-end multi-resolution
approach, a set of task-specific data augmentations, a modified loss function
using top-$k$, and spatial dropout. Furthermore, we devised a tailored
post-processing strategy. Ablation studies demonstrated that each of the four
modifications enhanced kidney abnormality segmentation performance, while three
out of four improved kidney parenchyma segmentation. Subsequently, we trained
the nnUNet framework on our dataset. By ensembling the optimized 3D U-Net and
the nnUNet with our specialized post-processing, we achieved marginally
superior results.
Our best-performing model attained Dice scores of 0.965 and 0.947 for
segmenting kidney parenchyma in two test sets (20 scans without abnormalities
and 30 with abnormalities), outperforming an independent human observer who
scored 0.944 and 0.925, respectively. In segmenting kidney abnormalities within
the 30 test scans containing them, the top-performing method achieved a Dice
score of 0.585, while an independent second human observer reached a score of
0.664, suggesting potential for further improvement in computerized methods.
All training data is available to the research community under a CC-BY 4.0
license on this https URL

使用深度学习方法，我们提出了一种肾脏实质和肾脏异常的分割方法，以帮助临床医生识别和量化肾脏异常，如囊肿、病变、肿块、转移和原发肿瘤。我们的方法通过训练 215 例胸腹部增强 CT 扫描，其中一半扫描含有一个或多个异常，实现了肾脏异常的准确分割和肾脏实质的改进分割。最佳模型的 Dice 分数在测试集中表现优异，超过了独立人眼观察者的分数，表明计算机化方法在肾脏异常分割中有进一步改进的潜力。