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Oral Cancer Detection Using Deep Learning

Name: Oral Cancer Detection Using Deep Learning
Brand: UpdateGadh
SKU: UG-97
Price: 2499.00 INR
Availability: InStock

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Oral Cancer Detection Using Deep Learning is a Python-based medical image classification project built with TensorFlow, Keras, Custom XceptionNet, and Flask. The system analyzes oral histopathological images and provides Cancer Detected or No Cancer Detected results with confidence scores, Grad-CAM visualization, tiled inference, and out-of-distribution image rejection.

Technologies: Python Machine Learning Bootstrap HTML/CSS

What You Will Get

Source Code

Database File

Project Report

PPT Presentation

Viva Questions

Setup Guide

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Oral Cancer Detection Using Deep Learning

Oral Cancer Detection Using Deep Learning is a Python-based image classification project designed for educational and research applications in medical image analysis. The system uses a custom XceptionNet convolutional neural network to process oral histopathological images and classify them into two categories: Cancer Detected and No Cancer Detected.

The trained deep learning model is integrated with a Flask web application, allowing users to upload images through a responsive browser interface and view prediction results. Along with the classification result, the application provides a confidence percentage and Grad-CAM visualization to show image regions that influenced the model's prediction.

Key Features

Histopathological image upload through a browser interface
Drag-and-drop image upload with client-side preview
File extension, image decoding, file-size, and dimension validation
Custom XceptionNet architecture using depthwise separable convolutions
Binary classification of cancer and non-cancer images
Confidence percentage and class probability results
Grad-CAM heat-map visualization
Out-of-distribution image rejection
299×299 tiled inference for large histopathological images
Top-k tile aggregation for final prediction
REST API for programmatic image prediction
Responsive Bootstrap 5 web interface
Model evaluation with multiple classification metrics

Technologies Used

Technology	Purpose
Python	Core programming and machine learning workflow
Flask	Web application and REST API
TensorFlow and Keras	Deep learning model development
Custom XceptionNet	Histopathological image classification
OpenCV	Image processing
Pillow	Image loading and preprocessing
scikit-learn	Machine learning utilities and evaluation
Bootstrap 5	Responsive frontend interface
JavaScript	Client-side functionality
Matplotlib	Visualization
pytest	Automated testing
Docker	Application deployment

More Details and Full Documentation

For complete project documentation, source information, dataset details, implementation guidance, and additional technical information, visit the original UpdateGadh project page.

View Complete Project Documentation

Note: This project is intended for educational and research purposes. Its predictions should not be considered a replacement for professional medical diagnosis or clinical decision-making.

Project Modules

1. Histopathological Image Upload Module
2. Image Validation Module
3. Image Preprocessing Module
4. Custom XceptionNet Model Module
5. Binary Cancer Classification Module
6. Confidence and Probability Analysis Module
7. Grad-CAM Visualization Module
8. Out-of-Distribution Image Rejection Module
9. Tiled Inference Module
10. Top-k Prediction Aggregation Module
11. Model Training Module
12. Model Evaluation Module
13. Flask Web Application Module
14. REST API Module
15. Automated Testing Module
16. Docker Deployment Module

Installation Guide

1. Install Python 3.10 or a compatible Python version supported by the project&amp;#039;s dependencies.
2. Open the Oral Cancer Detection project folder in Visual Studio Code.
3. Create a Python virtual environment using: python -m venv .venv
4. Activate the virtual environment on Windows using: .venv\Scripts\activate
5. Install the required dependencies using: pip install -r requirements.txt
6. Download the project dataset using: python training/download_dataset.py
7. Train the model using: python training/train_model.py --arch transfer --lr 1e-4 --epochs 30 --cache
8. Calibrate the domain guard using: python training/calibrate_domain_guard.py
9. Start the Flask application using: python app.py
10. Open http://127.0.0.1:5000 in your browser.
11. Upload a supported histopathological image through the prediction interface.
12. Review the classification result, confidence percentage, class probabilities, and Grad-CAM visualization.
13. Run the test suite using: pytest
14. For faster tests, use: pytest -m &amp;quot;not slow&amp;quot;
15. Generate test coverage using: pytest --cov=app --cov=training --cov-report=html
16. For Docker deployment, configure the production SECRET_KEY and start the application using Docker Compose.

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