• Prof. Pallavi Bharambe
• Preeti Patil
• Prajyot Wankhade
• Khushi Badgujar

We propose a novel multimodal medical image fusion method for brain tumors, integrating MRI and CT scans. Utilizing VGG-19 mesh-based fusion, our method generates LL, LH, LV, and LD bands for four image groups, eliminating redundant data. The approach demonstrates effectiveness in identifying brain lesions by employing deep convolutional neural networks like VGG-19. The method involves merging discrete wavelet-transformed images and reconstructing them using an adaptive transform. Evaluation is performed on collected brain MRI and CT images, showcasing the method's potential for enhanced diagnostic information.

Computed Tomography (CT), Multimodal medical image fusion, Brain tumor detection, VGG-19 algorithm, Magnetic Resonance Imaging(MRI).

"Intracranial Tumor Segmentation: An Advanced Software Tool for Brain Tumor Detection and Segmentation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.218-227, July-2024, Available :http://www.jetir.org/papers/JETIRGK06028.pdf

"Intracranial Tumor Segmentation: An Advanced Software Tool for Brain Tumor Detection and Segmentation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. pp218-227, July-2024, Available at : http://www.jetir.org/papers/JETIRGK06028.pdf