<![CDATA[The use of deep learning (DL) architectures like U-Net and GANs ensures secure, distributed model training across hospitals. The proposed work uses a privacy-preserving federated learning framework for emergency neuroimaging, enabling AI models to convert Computed Therapy (CT) scans into Magnetic Resonance Imaging (MRI) equivalent images as MRI images gives more accurate soft tissue details without compromising patient data. The proposed model integrates DL with saliency maps and Grad-CAM which are the Explainable AI (XAI) tools. The idea is to offer the transparency and build trust in diagnosis of disease. The image quality is measured using the metrics Structural Similarity Index (SSIM) and Paek Signal to Noise Ratio (PSNR) which ensures high-quality image synthesis. The proposed solution enhances the diagnostic accessability in resourse limited hospitals and rural hospitals by preserving patient data with standards. The enhanced model strengthens the framework, privacy techniques and secure aggregation techniques are used to prevent model data leakage during model training or updates. The study provides additional layer of protection to ensures using Federated Learning that even gradient-level information shared between hospitals cannot be traced back to individual patient data. The proposed system is scalable and enables integration with diverse hospital infractures and imaging modalities. The model provides the accessability by turning CT to MRI through secure XAI. The model accuracy ranges to 95% remaining validation accuracy close to train accuracy. The proposed idea provides emergency diagonistics with easy accesibility by preserving privacuy.]]>
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