Enhanced CNN Deep Learning for Efficient Processing of Big Medical Data in Cloud Environments

Abstract

The bursting emergence of digital technologies within healthcare systems has brought about a phenomenal expansion of medical information produced by electronic health records (EHRs), medical imaging systems, wearable technologies, genomics systems, telemedicine products, and hospital information systems. Big medical data is this massive and complicated sequence of structured and unstructured medical data which has a huge potential, but introduces several challenges. The conventional method of processing data can be problematic in relation to processing the scale, speed and diversity of such data sets especially when real time analytics and predictive knowledge is needed. Here, machine learning technologies and scalable cloud computing networks have been developed to a highly sophisticated level, and it provides a solution that is practical and transformational to the efficient processing, analysis, and significance of large-scale medical data. This paper is devoted to the adoption of sophisticated deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, transformers, and multifunctional architectures in combination with cloud computing technology in order to create scalable, secure, and intelligent pipelines of healthcare data processing. The proposed system uses distributed storage, parallel computing, and scalable cloud computing to learn and deploy deep learning models with large datasets of healthcare. Cloud-native features such as containerization, microservices architecture, and serverless computing have also been included in the system and have guaranteed the adaptability, cost-efficiency, and high availability. The system proposed is based on the data ingestion optimization, preprocessing, features extraction, model training, real-time inference, and interpretability of medical AI applications. The special consideration is given to the problem of data privacy, data security, regulatory and ethical concerns surrounding the use of AI. Some of the methods that have been embraced in order to guarantee the privacy of sensitive patient data and simultaneously allow more institutions to collaborate in creating models are federated learning, encryption, secure multi-party computation, as well as role-based access control. The performance comparison indicates that deep learning of cloud-based systems has decreased the training time significantly, improved scalability, and predictive performance. Besides, the explainable AI (XAI) techniques can increase the transparency and reliability of clinical decision-making. The suggested system will assist in the support of the clinical diagnosis, prediction of illnesses, treatment plans on the individual basis, and overall optimization of healthcare administration.