Effective Deep Learning Models for the Semantic Segmentation of 3D Human MRI Kidney Images

Recent studies indicate that millions of individuals suffer from renal diseases, with renal carcinoma, a type of kidney cancer, emerging as both a chronic illness and a significant cause of mortality. Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) have become essential tools for diagnosing and assessing kidney disorders. However, accurate analysis of these medical images is critical for detecting and evaluating tumor severity. This study introduces an integrated hybrid framework that combines three complementary deep learning models for kidney tumor segmentation from MRI images. The proposed framework fuses a customized U-Net and Mask R-CNN using a weighted scheme to achieve semantic and instance-level segmentation. The fused outputs are further refined through edge detection using Stochastic Feature Mapping Neural Networks (SFMNN), while volumetric consistency is ensured through Improved Mini-Batch K-Means (IMBKM) clustering integrated with an Encoder-Decoder Convolutional Neural Network (EDCNN). The outputs of these three stages are combined through a weighted fusion mechanism, with optimal weights determined empirically. Experiments on MRI scans from the TCGA-KIRC dataset demonstrate that the proposed hybrid framework significantly outperforms standalone models, achieving a Dice Score of 92.5%, an IoU of 87.8%, a Precision of 93.1%, a Recall of 90.8%, and a Hausdorff Distance of 2.8 mm. These findings validate that the weighted integration of complementary architectures effectively overcomes key limitations in kidney tumor segmentation, leading to improved diagnostic accuracy and robustness in medical image analysis.