Deep Learning-Based Brain Tumor Detection in Privacy-Preserving Smart Health Care Systems

Deep learning has been widely used in medical image processing, which has sparked the development of a wide range of applications and led to a notable increase in the number of therapeutic and diagnostic options available for a range of medical imaging problems. In the era of the Internet of Things (IoT), safeguarding the security and privacy of medical data is crucial to the advancement of sophisticated diagnostic applications for medical imaging. Deep learning-based brain tumor detection in smart health care systems with privacy preservation is proposed in this paper. The system under consideration is organized into three discrete stages that are then combined to provide an all-encompassing blueprint. During the first phase, patients with brain tumors are the primary target of an efficient healthcare system that is introduced. A Microsoft-based operating system-compatible application has been developed to accomplish this. Patient data is secure and only available to the hospital and the individual patient, which enables patients to engage with the system both locally and virtually. To obtain the anticipated outcomes, the user must first submit the patient’s MRI scan and then enter a special 10-digit code. In the second part, the authors develop a deep learning-based tumor identification platform which also incorporates the AES-128 algorithms and PBKDF2 for secure medical image storage on the server and data transmission via the internet from the client to the server and back to the client upon prediction. The proposed approach integrates ResNet-50, Inception V3, and VGG-16 architecture to build a Convolutional Neural Network (CNN)-based brain tumor diagnosis system. These architectures are enhanced through significant pre-processing, SGD, RMSprop, and Adam optimization. Our research focuses on the application of cutting-edge methods to maintain confidentiality and accomplish precise tumor diagnosis, underscoring the importance of privacy preservation. Our micro-average findings were the best, with 99.92% accuracy, 99.99 % Area Under the Curve (AUC), 99.9 % precision, 99.92 % recall, and 99.92 % F1-score. Moreover, significant influence on tumor categorization was demonstrated when the experimental outcomes of the modified models were contrasted with multiple CNN-based architectures through the use of critical performance criteria.