AI Driven Disease-Specific Adaptive Multimodal Medical Image Fusion with CNN

Abstract

Despite the utilization of multimodal medical imaging as supplementary anatomical and functional data crucial for precise illness diagnosis, the appropriate integration of multimodal pictures has been challenging due to discrepancies in resolution, contrast, and disease-specific imaging characteristics. Most established techniques for image fusion are modality-specific, depend on manually crafted features, and lack the adaptability to encompass a wide variety of pathological presentations, hence constraining their clinical applicability. This paper proposes an AI-driven, disease-specific, adaptive multimodal medical image fusion utilizing Convolutional Neural Networks (CNNs) to tackle these challenges. This strategy is proposed to be an end-to-end trained modality-specific and pathology-aware feature capable of generating adaptive fusion to increase clinically significant areas, excluding structural characteristics. The qualitative and quantitative assessments reveal that the experimental results of multimodal medical image datasets indicate that the proposed method surpasses both traditional and sophisticated deep learning-based fusion techniques. The performance metrics of PSNR, SSIM, entropy, and mutual information, which indicate enhanced fusion quality, contrast, and diagnostic clarity, substantiate the effectiveness of the proposed framework in facilitating disease-oriented clinical decision-making and computer-aided diagnostics systems.