Classification and segmentation of kidney MRI images for chronic kidney disease detection

Chronic Kidney Disease (CKD) is a common ailment with significant public health implications, underscoring the critical importance of early detection and diagnosis for effective management. Our research focuses on utilizing deep learning models to accurately classify and segment kidney MRI images, aiding in the timely detection and diagnosis of CKD. Utilizing the Zenodo dataset comprising 1299 kidney MRI images from 100 T2-weighted abdomen MRI scans, our investigation reveals that Vision Transformers (ViT) based model EfficientNet_b1 exhibited superior performance compared to other models. It achieved an accuracy of 94.38% in distinguishing between healthy and CKD cases in kidney MRI image classification. Moreover, the pretrained models Densenet201 and VGG19, as well as the ViT-based models EfficientNet_b1 and tf_mobilenetv3_large_075, demonstrate remarkable sensitivity and accuracy while compared to identifying kidney MRI images which highlights the effectiveness of deep learning architectures in identifying diseases. Additionally, our segmentation analysis reveals that our proposed network, Resnet18-Self-ONN-UNet++, adeptly delineates kidney structures. The network shows remarkable performance in medical diagnosis and treatment planning, with an Intersection over Union (IoU) of 82.34% and DS of 91.57% when integrated with the Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing technique and Simultaneous truth and performance level estimation (STAPLE) as post processing technique. In summary, deep learning-driven kidney MRI image classification and segmentation enhance medical imaging analysis, potentially aiding in early CKD diagnosis and treatment, thereby addressing critical public health concerns, and enhancing patient outcomes.