Cloud-Integrated Wavelet Transform and Particle Swarm Optimization for Automated Medical Anomaly Detection

Abstract

Cloud computing and Artificial Intelligence (AI) have played a vital role in transforming automated detection of medical anomalies, ensuring real-time diagnoses, well-informed decision-making, and increased patient quality of care. However, a gamut of challenges exists in cloud-based medical systems, such as maintenance of data integrity, privacy concerns, and risks associated with security issues. The traditional systems of anomaly detection have faced limitations associated with very low accuracy levels, primarily by way of ineffective feature extraction. Better feature selection mechanisms have also been of supreme importance. Typically, the currently used machine learning models were developed from traditional models, which suffered a lot with high-dimensional medical data and had very little generalization capacity. Also, security loopholes in cloud-based systems may compromise patient data privacy and security, while real-time data processing constraints may not help detect the anomaly on time, sometimes hurting clinical decision-making. An attempt was made in the current research to introduce a very novel framework that integrates Wavelet Transform (WT) and Particle Swarm Optimum (PSO) for automating medical anomaly detection within the cloud. WT, as a new tool, carries out multiresolution analysis to extract strongly advocating critical features from medical images, while PSO contributes to optimizing feature selection for improving the classification accuracy. Several machine-learning-based models are utilized for effective classification of anomalies such as SVM, Random Forest, and Deep Learning. The proposed system integrates advanced encryption techniques, two or multisegmented authentication, along with AI-based real-time detection of threats to ensure complete security within a cloud environment. Its performance evaluation against well-acknowledged benchmark datasets, becomes concrete by virtue of observing the accuracy, sensitivity, specificity, and false discovery rate. The results show that the WT-PSO hybrid solution would greatly improve anomaly detection capability while also securing the system more securely, ensuring scalability and real-time processing capabilities within cloud-based medical systems.