Depth-ResNet – Polyp Image Classification Tool
DepthResNet – Polyp Image Classification Tool using Depth-ResNet (Cross-Platform)
Developed DepthResNet, a cross-platform GUI-based application for the automatic classification of polyp images by a customized convolutional neural network named Depth-ResNet. This project is made to assist medical professionals and researchers with fast and accurate identification of polyps from endoscopic images.
The core of the application is Depth-ResNet, a customized extension of the ResNet50 architecture, specifically customized to identify patterns and subtle features unique to polyp structures. This model was trained on a diverse dataset of labeled polyp images.
The trained model is integrated into a custom-built GUI application that works on both Windows and Linux systems. The interface allows users to load images, receive real-time classification results, and optionally model predictions with confidence scores or heatmaps.
Use Case: Ideal for use in medical research labs, clinical screening tools, academic projects, and educational settings.
Key Features
- Depth-ResNet: A custom deep learning model based on ResNet50, optimized for polyp classification.
- Cross-platform GUI application supporting both Windows and Linux systems.
- Real-time classification with optional confidence score.
- Built for practical clinical and research integration.
Ongoing Work: The project is currently in its research and development phase, with ongoing efforts to improve speed and accuracy for clinical deployment.
Note: More information about this project will be added soon.