Enhancing Medical Diagnosis Through Multimodal Medical Image Fusion

Abstract

In the medical domain, multimodal image fusion has emerged as a powerful technique aiming to enhance diagnostic accuracy and clinical decision-making. Image fusion technique combines two or more images from the different imaging modalities to enhance image detail and preserve information. However, single modality imaging fails to provide accurate information necessary for precise analysis and diagnosis. This chapter introduces a flask-based application that integrates multiple medical images, merging brain CT scans and MRI through landmark-based image registration. Then wavelet transform-based fusion techniques combine the registered images, providing a comprehensive view of the brain's neural structure and functions. A CNN model is then employed to identify brain tumors in the fused multimodal images. Following tumor detection, the model categorizes tumors as glioma, meningioma, or pituitary tumor. Through the incorporation of these methodologies, the application supports medical imaging and diagnosis by enhancing accuracy, efficiency, and clinical outcomes.