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MATLAB

Cancer-Diagnosis-On-the-Human-Brain

What the project does**

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System Overview

What the project does

An AI‑powered tool that analyzes medical brain imaging (e.g., MRI scans) to detect and classify cancerous lesions, providing automated diagnostic assistance for clinicians.

Key features

  • - Pre‑processing pipeline for MRI normalization, skull‑stripping, and noise reduction
  • - Deep convolutional neural network (CNN) trained on labeled brain tumor datasets
  • - Real‑time inference with probability scores and lesion segmentation overlays
  • - Performance metrics dashboard (accuracy, sensitivity, specificity, AUC)
  • - Exportable reports in PDF/JSON for integration with electronic health records (EHR)

    • Tech stack

  • - Python 3.x, PyTorch/TensorFlow for model development
  • - OpenCV & NiBabel for image handling
  • - Flask API (or FastAPI) for serving predictions
  • - Docker for containerization; optional Kubernetes deployment
  • - Front‑end: React + Plotly for interactive visualizations

    • Use case

    Enables radiologists and oncologists to quickly screen brain MRI scans for tumors, improving early detection rates, reducing manual review time, and supporting diagnostic decisions in hospitals or tele‑medicine platforms.

    Architecture Details

    This system integrates multiple components for a seamless automation flow. Structural interpretation based on project focus:

    Backend Infrastructure

    Core execution layer for robust data processing and API handling.

    AI / Logic Core

    Intelligent decisioning via models or logical workflow rules.

    Tech Stack

    MATLABIntegrationAutomationAPIs

    Key Capabilities

    • ▹ Custom workflow execution
    • ▹ Data transformation and routing
    • ▹ Extensible architecture