Machine Learning for Multi-Class Kidney Tumour Segmentation: Advancing Classification Accuracy

Appropriate treatment planning depends heavily on early detection together with accurate sectioning of kidney tumours. The research design introduces a deep learning architecture which combines convolutional neural networks (CNNs) along with transformer-based modes to advance both multi-class kidney tumour segmentation and classification precision. Multimodal CT and MRI data serves as an input for this model because it solves issues stemming from tumour heterogeneity and overlapping areas. The proposed framework showed 95% total classification precision along with 92% Dice coefficient accuracy for clear cell renal carcinoma detection during clinical testing of various public and medical datasets above baseline V-Net and 3D U-Net models. An evaluation of performance speed and operational constraints (such as resource needs) takes place. The obtained results illustrate how artificial intelligence reduces the necessity for human supervision to enhance diagnostic assurance. Future research will concentrate on developing real-time system deployment along with interpretation improvements to make the system suitable for medical use.