Kidney Abnormality Detection Using Segmentation-Guided Classification on Computed Tomography Images

Kidney cancer currently has the 14 th highest incidence rate globally. Abnormal growths such as tumors and cysts in the kidney can be malignant and lead to cancer. The current method employed by nephrologists is manually locating the tumors and cysts from CT scans. However, due to the high incidence rate of kidney cancers, this time-consuming approach puts pressure on nephrologists and may lead to misdiagnoses. To improve the diagnostic process, this study proposes an automatic segmentation-guided classification method that can identify and differentiate between normal kidney areas and abnormal kidney areas that contain tumors, cysts, or both. Thereby, the model can detect abnormal kidney regions. In the proposed method, firstly, TotalSegmentator segmentation model's kidney masks are used to crop kidney regions from the abdominal CTs while generating 2D axial images. Then, ResNet 101 is used to classify the images into ‘normal’ and ‘abnormal’ classes. The proposed method has a high performance in all metrics with a score of 0.9626 and 0.8670 in recall and precision, respectively.