Enhanced Deep Learning Techniques for Multimodal Medical Image Fusion

Abstract

Multimodal Medical Image Fusion is the process of integrating data from various imaging modalities to produce a full and improved analysis of a patient's medical condition. MRI, CT, and PE T scans are examples of medical imaging modalities that can be used to produce a more thorough and informative image for better disease monitoring, treatment planning, and diagnosis. Because deep learning can understand the links between the input modalities and the intended fused image, it has become a potent tool for medical image fusion. Deep learning is a subfield of artificial intelligence. Computed tomography (CT) and magnetic resonance imaging (MRI) are two popular medical imaging technologies that provide complementary insights into the human body's internal components. While CT employs X-rays to provide cross-sectional images of bones, tissues, and organs with high spatial resolution and quick imaging, MRI uses magnetic fields and radio waves to make detailed images of soft tissues and organs with great contrast resolution. This paper explains a few of the deep learning techniques like DeepFuse technique, ResN et architecture, Generative Adversarial Networks (GANs), Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) model, Autoencoders, and Attention mechanics are some of the deep learning methods for MRI and CT image fusion that are currently in use. Better visualization and delineation of anatomical features are made possible by these deep learning-based fusion approaches, which eventually improve patient diagnosis, treatment, and overall health care results.