Deep Learning based Multimodal Image Fusion for Brain Tumor Diagnosis: A Comprehensive Review

Abstract

Deep learning-based brain tumor classification has grown exponentially, revolutionizing medical diagnosis by automating and achieving high accuracy. Multimodal image fusion (MMIF) has emerged as a critical technique in intelligent healthcare systems, especially for brain tumor detection. MRICT image fusion enhances diagnostic performance by taking advantage of the high soft-tissue contrast of MRI and the fine anatomical structure of CT. Traditional fusion approaches commonly experience information loss, redundancy, and low generalizability across diverse imaging modalities. The recent advancements in deep learning have greatly improved MMIF through data-driven feature extraction, adaptive fusion techniques, and strong classification algorithms. The study presents a detailed review of advanced deep learning-based MMIF methods for brain tumor analysis such as CNNs, autoencoders, GANs, and transformer-based designs. It also investigates attention mechanisms, multi-scale fusion, and hybrid models in order to enhance both spatial and semantic consistency. In addition, the utilization of explainable AI (XAI) is highlighted to establish clinical trust and model explanation interpretability. Data sparsity, lack of generalizability, and intensive computational requirements are some of the challenges highlighted, with some of the most promising solutions including few-shot learning, federated learning, and real-time inference systems. Through the incorporation of recent breakthroughs, this survey seeks to inform the development of efficient, interpretable, and scalable MMIF architectures for state-of-the-art AI-based clinical diagnostics.