DEEP NEURAL NETWORKS FOR MULTI-MODAL IMAGE ANALYSIS: EXPANDING THE FRONTIERS OF AI-ENABLED VISUAL UNDERSTANDING

Abstract

Deep Neural Networks (DNNs) have revolutionized the field of artificial intelligence (AI) and image processing, enabling enhanced analysis and interpretation of visual data. This research article focuses on the application of DNNs in multi-modal image analysis, which involves integrating and analyzing information from different imaging modalities. By exploiting the complementary nature of multi-modal data, DNNs push the frontiers of AI-enabled visual understanding, leading to improved image analysis outcomes. The article explores various aspects, including network architectures, feature extraction and fusion techniques, training algorithms, and real-world applications in domains such as medical imaging, remote sensing, and autonomous systems. Through the advancement of DNNs in multi-modal image analysis, this research article showcases the potential for expanding the boundaries of visual understanding and its impact on diverse fields.