metadata
license: mit
pipeline_tag: image-classification
base_model: google/vit-base-patch16-224
tags:
- medical-ai
- image-classification
- pneumonia
- chest-xray
- gradcam
- vit
🫁 Pneumonia Detector
🎯 Live Demo
👉 Try it on Hugging Face Spaces
🔍 Overview
A medical image classification system that detects pneumonia from chest X-ray images using Vision Transformer (ViT) with Grad-CAM explainability. The model highlights which regions of the X-ray influenced the prediction, making it transparent and trustworthy for medical screening.
📊 Results
| Metric | Score |
|---|---|
| Overall Accuracy | 87% |
| AUC-ROC | 0.9805 |
| PNEUMONIA Recall | 1.00 |
| NORMAL Precision | 0.99 |
📈 Classification Report
| Class | Precision | Recall | F1-Score |
|---|---|---|---|
| NORMAL | 0.99 | 0.66 | 0.79 |
| PNEUMONIA | 0.83 | 1.00 | 0.91 |
✨ Features
- ✅ Chest X-ray classification: NORMAL vs PNEUMONIA
- ✅ Vision Transformer (ViT) fine-tuned on medical images
- ✅ Grad-CAM heatmap visualization for explainability
- ✅ Handles class imbalance with weighted loss
- ✅ Gradio demo for interactive predictions
🏗️ Architecture
Chest X-Ray Image → Preprocessing (224x224) → ViT Base Patch16 → Fine-tuned Classifier Head → Prediction + Grad-CAM Heatmap
🛠️ Tech Stack
| Component | Tool |
|---|---|
| Model | Google ViT Base Patch16 224 |
| Framework | PyTorch |
| Explainability | Grad-CAM |
| Dataset | Chest X-Ray Images (Pneumonia) — Kaggle |
| UI | Gradio |
| Training | Google Colab T4 GPU |
📁 Dataset
- Source: Chest X-Ray Images (Pneumonia)
- Total Images: 5,863
- Classes: NORMAL (1,341) vs PNEUMONIA (3,875)
- Split: Train / Val / Test
🚀 How to Use