OncoVision API

OncoVision API is a deep learning–based medical image analysis system that detects cancer from medical images (histopathology / X-ray / MRI depending on dataset) using a Convolutional Neural Network (CNN).
OncoVision API is a backend-only academic project that performs cancer detection from medical images using a Convolutional Neural Network (CNN).

The system accepts file uploads of multiple types, safely converts them into CNN-compatible images, and performs inference using a pretrained model.

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  Key Characteristics
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  - Backend only (FastAPI) 
  - CNN-based inference (no transformers, no LLMs) 
  - Fully offline (no cloud APIs) 
  - File-based input only (multipart upload) 
  - Supports images and documents 
  - Beginner-friendly but architecturally clean 
  - Academic + production-aligned design
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Architecture Overview

Client -> FastAPI Route -> Inference Service -> File Converter -> Image Preprocessing -> CNN Prediction -> Structured JSON Response

Core Design Principle: The CNN only works on images. All non-image files are converted into images before inference.

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  Supported Input Types
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  Images: JPG, JPEG, PNG Documents: PDF, DOCX
  Others: Rejected
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API Response Format

{
 “status”: “success”,
 “file_type”: “pdf”,
 “images_processed”: 3,
 “prediction”: “Cancer Detected”,
 “confidence”: 0.87 
}

backend/
│
├── app/
│   ├── main.py                # FastAPI entry point
│   ├── config.py              # Settings (paths, model name)
│   │
│   ├── api/
│   │   ├── routes.py           # API endpoints
│   │   └── schemas.py          # Request/Response models
│   │
│   ├── model/
│   │   ├── cnn_model.py        # CNN architecture
│   │   ├── load_model.py       # Load trained model
│   │   └── predict.py          # Prediction logic
│   │
│   ├── utils/
│   │   ├── file_converter.py
│   │   ├── image_utils.py      # Resize, normalize, tensor conversion
│   │   └── logger.py
│   │
│   └── services/
│       └── inference.py        # Business logic wrapper
│
├── model_weights/
│   └── cancer_cnn.h5           # Saved model
│
├── requirements.txt
├── README.md
└── .env

Academic Disclaimer

This project is for educational purposes only. It is not a medical device and must not be used for real-world diagnosis.