A Review of Deep Learning-based Multi-modal Medical Image Fusion

Medical image fusion combines the data obtained from different imaging modalities such as Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI) into a single, informative image that aids clinicians in diagnosis and treatment planning. No single imaging modality can provide complete information on its own. This has led to the emergence of a research field focused on integrating data from multiple modalities to maximize information in a single, unified representation.

CNN (Convolutional Neural Network) was applied to achieve robust and effective multi-modal image fusion. By delving into the principles and practical applications of this deep learning approach, the paper also provides a comparative analysis of CNN-based results with other conventional fusion techniques.

CNN-based image fusion delivers far better results in terms of qualitative and quantitative analysis when compared with other conventional fusion methods. The paper also discusses future perspectives, emphasizing advancements in deep learning that could drive the evolution of CNN-based fusion and enhance its effectiveness in medical imaging.

CNN-based multi-modal medical image fusion proves strong advantages over traditional methods in terms of feature preservation and adaptability. However, challenges such as data dependency, computational complexity, and generalization across modalities persist. Emerging trends like attention mechanisms and transformer models show promise in addressing these gaps. Future work should focus on improving interpretability and clinical applicability, ensuring that deep learning fusion methods can be reliably integrated into real-world diagnostic systems.

Ultimately, this work underscores the potential of CNN-based fusion to improve patient outcomes and shape the future of medical imaging by advancing the understanding of multi-modal fusion.