Deep learning has achieved strong performance in MRI-based brain tumor classification; however, existing systems largely ignore the security, privacy, and operational reliability of clinical AI pipelines. This work presents a unified diagnostic–security framework that integrates convolutional neural network–based tumor classification with generative artificial intelligence–driven security information and event management (SIEM) within a single end-to-end clinical workflow. The diagnostic module employs a VGG16 transfer-learning architecture trained on 7,023 multi-institutional MRI scans across four classes (glioma, meningioma, pituitary tumor, and healthy tissue), achieving a test accuracy of 95.73% and a weighted F1-score of 0.96. To secure inference and explanation artifacts, the framework introduces a privacy-preserving logging pipeline combining Gaussian differential privacy, Paillier homomorphic encryption, and federated log aggregation. A Transformer–VAE hybrid model is adapted for high-dimensional medical event correlation and missing-log reconstruction. System-level feasibility is evaluated using OMNeT++ network simulation for multi-hospital traffic modeling and Simulink for real-time pipeline latency analysis. Experimental results demonstrate encrypted log correlation latency of 189 ms, anomaly detection accuracy of 91.8%, and stable diagnostic throughput under adversarial network conditions exceeding 1.4 million log events. The novelty of this work lies in the system-level integration of diagnostic AI and generative security analytics for clinical imaging workflows, enabling secure, explainable, and deployable medical AI under realistic hospital constraints...
Keywords: Deep Learning, VGG16, Brain Tumor Detection, Medical Imaging, MRI Analysis, Generative AI, SIEM, Transformer-VAE Hybrid, Differential Privacy, Homomorphic Encryption, OMNeT++, Simulink, Secure Clinical Workflows.
How to cite this article:Mishra S, Sabut SK.; Towards Secure and Intelligent Clinical Imaging: A Unified Deep Learning and Generative AI Framework for Brain Tumor Diagnosis..Int J Drug Deliv Technol. 2026;16(1s): 693-705; DOI: 10.25258/ijddt.16. 693-705