Monish Khan

Abstract Accurate brain tumor classification from magnetic resonance imaging (MRI) is critical for early diagnosis and effective clinical decision-making. Although recent CNN–Transformer hybrid models have shown promising performance, most approaches rely primarily on single-modal spatial information, limiting their ability to capture complementary spectral features, model tumor heterogeneity, and generalize across datasets.

Abstract The rapid expansion of IoT-based healthcare systems has led to an explosion of multimodal patient data (e.g., voice, images, signals), raising concerns about accurate classification, real-time processing, and data security. Existing machine learning approaches often suffer from limited feature extraction, high computational demands, and inadequate privacy protection, particularly in decentralised and resource-constrained environments.