https://github.com/tejaswinisgit/diagnosis
Real-time Identification of Pneumonia from X-ray Images using Deep Learning
https://github.com/tejaswinisgit/diagnosis
cnn codeclause-internship deep-learning healthcare-ai keras medical-image-analysis pneumonia tensorflow xray
Last synced: 4 months ago
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Real-time Identification of Pneumonia from X-ray Images using Deep Learning
- Host: GitHub
- URL: https://github.com/tejaswinisgit/diagnosis
- Owner: TejaswinisGit
- Created: 2025-06-26T07:52:02.000Z (4 months ago)
- Default Branch: master
- Last Pushed: 2025-06-26T08:53:23.000Z (4 months ago)
- Last Synced: 2025-06-26T09:38:06.840Z (4 months ago)
- Topics: cnn, codeclause-internship, deep-learning, healthcare-ai, keras, medical-image-analysis, pneumonia, tensorflow, xray
- Language: Python
- Homepage: https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia
- Size: 6.84 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🩺 Pneumonia Diagnosis from Chest X-rays using CNN
This project uses **Convolutional Neural Networks (CNNs)** to classify chest X-ray images as either **Pneumonia** or **Normal**. It was developed as part of my internship to explore deep learning in medical image analysis.
By training the model on labeled chest X-ray data, it can automatically learn the visual patterns associated with pneumonia, helping in faster and automated preliminary diagnosis.
---
## 📌 Highlights
- 📂 Dataset: Chest X-ray images (2 classes: Pneumonia, Normal)
- 🧠 Model: CNN built using TensorFlow and Keras
- 🖼️ Input: Grayscale images resized to (150x150)
- ⚙️ Training & Validation: Using Keras ImageDataGenerator
- 📈 Output: Accuracy and loss graphs
---
## 🚀 How to Run the Project
### 1. Clone the repository:
```bash
git clone https://github.com/TejaswinisGit/Diagnosis.git
cd Diagnosis
```
### 2. Install required libraries:
```bash
pip install tensorflow matplotlib numpy
```
### 3. Prepare dataset:
- Place your X-ray images in a `chest_xray` folder with this structure:
```
chest_xray/
├── train/
│ ├── NORMAL/
│ └── PNEUMONIA/
├── val/
│ ├── NORMAL/
│ └── PNEUMONIA/
└── test/
├── NORMAL/
└── PNEUMONIA/
```
### 4. Run the script:
```bash
python Diagnosis.py
```
---
## 📚 What I Learned
Through this project, I gained practical experience in:
- Preprocessing real-world medical image data
- Using CNNs to detect visual patterns
- Understanding data augmentation and model overfitting
- Applying deep learning for binary classification problems
This project helped me understand how deep learning models can be applied to the healthcare domain for faster diagnostics.
---
## 👩💻 About Me
I'm **Tejaswini Thungathoorthi**, a Computer Science and Engineering student specializing in **AI and Machine Learning**. I'm passionate about using technology to solve real-world problems, and this project is a step toward impactful healthcare solutions using AI.
---
## 📬 Contact
- 📧 Email: [tejaswini17109@gmail.com]
- 💼 LinkedIn: [https://www.linkedin.com/in/tejaswini-thungathoorthi-9076b2295/]