Computational Intelligence for Multimodal Analysis of High‐Dimensional Image Processing in Clinical Settings
B. Balaji, P. Pugazhendiran, N. Sivanantham, Navaneetha Velammal, P. Vimala · 2024
Artificial intelligence (AI) has contributed significantly to health research and imaging technology advancements. Biomedical image processing leverages techniques such as image processing, machine learning, computer vision, supervised learning, big data analytics, and cloud technology. The medical industry utilizes fuzzy logic, Bayesian inference frameworks, statistics parts-based models, and reinforcement learning-based multistage picture segmentation methods. These technologies play a crucial role in computer-assisted medical disease detection and enable biomedical data analysts to interpret complex medical data, thus enhancing patient care. Biological data processing faces challenges such as high dimensionality, class imbalance, and limited database capacity. While traditional data collection methods are effective, modern technology offers superior solutions. Accurate diagnosis of interconnected diseases requires high-dimensional biological data and exceptional images. Cloud computing's storage and processing power are utilized to analyze biological data and construct globally accessible systems. DICOM-compliant cloud computing systems store and translate images into high-quality data, enabling the analysis of complexity-rich biomedical data models through multimodal techniques. Artificial intelligence strives to provide high-quality data, accurate models, and straightforward solutions to complex challenges in biomedical processing.