https://github.com/arpanpramanik2003/chest-xray-pneumonia-classifier
🔍 A deep learning system that analyzes chest X-rays to detect pneumonia with 95.4% accuracy. Built with TensorFlow/Keras and deployed via Streamlit. NOT for clinical diagnosis.
https://github.com/arpanpramanik2003/chest-xray-pneumonia-classifier
chest-xray-images classification deep-learning high-performance keras pneumonia-detection streamlit tensorflow
Last synced: 2 months ago
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🔍 A deep learning system that analyzes chest X-rays to detect pneumonia with 95.4% accuracy. Built with TensorFlow/Keras and deployed via Streamlit. NOT for clinical diagnosis.
- Host: GitHub
- URL: https://github.com/arpanpramanik2003/chest-xray-pneumonia-classifier
- Owner: arpanpramanik2003
- License: mit
- Created: 2025-04-09T18:29:32.000Z (7 months ago)
- Default Branch: master
- Last Pushed: 2025-04-10T12:31:32.000Z (7 months ago)
- Last Synced: 2025-07-23T16:56:04.264Z (3 months ago)
- Topics: chest-xray-images, classification, deep-learning, high-performance, keras, pneumonia-detection, streamlit, tensorflow
- Language: Jupyter Notebook
- Homepage: https://chest-xray-pneumonia-classifier-arpan.streamlit.app/
- Size: 496 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Pneumonia Detection from Chest X-rays
## 🚀 Overview
A deep learning system that accurately classifies chest X-ray images as **Normal** or **Pneumonia** using convolutional neural networks. The system demonstrates hospital-grade accuracy in detecting bacterial and viral pneumonia patterns from radiographic images.
## 💻 Technical Stack
- **Deep Learning Framework**: TensorFlow 2.12 + Keras
- **Base Model**: Custom CNN with Transfer Learning (VGG16 backbone)
- **Interface**: Streamlit web application
- **Image Processing**: OpenCV + Pillow
- **Data Source**: Kaggle Chest X-Ray Images (Pneumonia) dataset
## 📈 Performance Metrics
| Metric | Validation | Test Set |
|---------------|------------|----------|
| Accuracy | 96.2% | 95.4% |
| Precision | 96.8% | 96.1% |
| Recall | 95.5% | 95.8% |
| F1-Score | 96.1% | 95.9% |
| AUC-ROC | 0.993 | 0.991 |
## ✨ Key Features
- **Clinical-Grade Accuracy**: >95% test accuracy comparable to radiologists
- **Real-Time Analysis**: Processes X-rays in <2 seconds
- **Explainable AI**: Integrated Grad-CAM heatmaps for visual interpretation
- **Responsive Design**: Mobile-friendly interface for point-of-care use
- **Confidence Scoring**: Detailed probability outputs (0-100%)
- **Multi-Format Support**: Accepts JPG/PNG/DICOM images
## ⚠️ Important Notes
- Not intended as primary diagnostic tool - always consult physicians
- Performance may vary with unconventional X-ray machines
- Model trained on pediatric cases (1-5 years age group)
## 📜 License
MIT License
Copyright (c) 2023 [Arpan Pramanik]
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.