Addressing Data Quality, Ethical, Privacy, and Interoperability Challenges in Real-time Viral Disease Prediction Using Convolutional Neural Networks on X-ray Imagery
Abstract:
The swift rise of viral illnesses necessitates inventive approaches for quick and precise identification. Convolutional Neural Networks (CNNs) have demonstrated potential in scrutinizing viral X-ray images, but several obstacles exist, such as issues related to data integrity, ethical considerations, privacy protections, seamless integration, and real-time computational needs. The goal of this research is to surmount these obstacles by crafting a resilient CNN algorithm for instantaneous viral disease detection based on X-ray data. We introduce a comprehensive framework for preliminary data cleansing, aimed at enhancing the consistency and quality of information from varied medical systems. Ethical and privacy hurdles are mitigated through the application of differential privacy methods and a well-structured consent system to maintain patient confidentiality. We utilize universally accepted standards to foster data exchangeability, thus permitting instantaneous sharing of data across different platforms. Our CNN algorithm is optimized for swift processing, facilitating almost immediate diagnostic advice. Initial findings show considerable advancements in both the speed and precision of diagnoses, situating this study at the nexus of medical care and machine learning technologies. This research seeks to establish new standards in confronting present-day issues affecting the deployment of data science within the healthcare landscape, particularly concerning viral infections.
