COMPREHENSIVE BRAIN TUMOR SEGMENTATION APPROACHES WITH MULTIMODAL IMAGE FUSION TECHNIQUES USING CNN

Abstract

Accurate segmentation of brain tumors from medical imaging data is crucial for diagnosis , treatment planning and monitoring of brain tumor patients. This research explores the application of convolutional neural networks (CNNs) for brain tumor segmentation , focusing on the integration of multimodal imaging techniques. Specifically , we investigate the fusion of various imaging modalities such as Magnetic Resonance Imaging (MRI) and CT scans (Computed Tomography) to improve segmentation accuracy and robustness. Our approach involves preprocessing techniques for data normalization and augmentation, followed by the design & training of CNN architectures tailored for multimodal image fusion. Evaluation metrics include Dice coefficient , sensitivity and specificity , comparing our method with traditional single modality segmentation approaches. This study conducts a comparative exploration of CNN model,  including HRnet, U-net and ResUnet architectures. Experimental results demonstrate that our approach of multimodal image fusion using CNN achieves superior segmentation performance, effectively handling variations in tumor appearances across different imaging modalities. Experimental results on real-world data demonstrates that our approach of multimodal image fusion using Deep Neural Networks achieves superior segmentation performance, with accuracies of 99.49% for HRNet, 98.10% for ResUNet, and 99.48% for UNet, effectively handling variations in tumor appearances across different imaging modalities. This research contributes to advancing state-of-the-art in brain tumor segmentation, offering a comprehensive framework that can potentially enhance clinical decision-making and patient care.