Deep learning medical imaging pipeline integrating CT and MRI scans through preprocessing, CNN feature extraction, and hybrid supervised-unsupervised models for tumor detection and classification. Data flows from input images through resize, noise removal, and normalization stages, then splits into CNN/ResNet and Autoencoder/Clustering branches for benign/malignant analysis. Results are stored in a database and surfaced via a visualization dashboard and doctor panel, enabling clinicians to access DICOM/NIfTI images, JSON/HL7 reports, and tensor-based insights. This architecture demonstrates best practices for production medical AI: modular preprocessing, ensemble learning, and HIPAA-ready output formats. Fork and customize this diagram on Diagrams.so to adapt preprocessing steps, swap model architectures, or integrate your hospital's PACS system.