README.md

# 🩺 Chest X-Ray Pneumonia Detection using Deep Learning

<div align="center">

![Python](https://img.shields.io/badge/Python-3.10-blue?style=for-the-badge&logo=python)
![TensorFlow](https://img.shields.io/badge/TensorFlow-2.x-orange?style=for-the-badge&logo=tensorflow)
![Keras](https://img.shields.io/badge/Keras-Deep%20Learning-red?style=for-the-badge&logo=keras)
![Flask](https://img.shields.io/badge/Flask-Backend-black?style=for-the-badge&logo=flask)
![Streamlit](https://img.shields.io/badge/Streamlit-Web_App-ff4b4b?style=for-the-badge&logo=streamlit)
![OpenCV](https://img.shields.io/badge/OpenCV-Image_Processing-green?style=for-the-badge&logo=opencv)
![CNN](https://img.shields.io/badge/CNN-Custom_Model-success?style=for-the-badge)
![VGG16](https://img.shields.io/badge/VGG16-Transfer_Learning-blueviolet?style=for-the-badge)

</div>

---

# 📌 Project Overview

This project is an **AI-powered Chest X-Ray Pneumonia Detection System** that automatically classifies chest X-ray images into:

- ✅ NORMAL
- 🦠 PNEUMONIA

The project demonstrates multiple Deep Learning approaches and compares their performance using:

- Custom CNN (10 Epochs)
- Improved CNN (20 Epochs)
- Transfer Learning (VGG16)

The trained models are deployed using **Flask**, while the frontend is developed using **Streamlit**, creating a complete end-to-end AI medical imaging application.

---

# 🚀 Features

✔ Binary Classification

✔ Three Deep Learning Models

✔ Custom CNN Architecture

✔ Improved CNN Architecture

✔ Transfer Learning using VGG16

✔ Real-time Image Prediction

✔ Flask REST Backend

✔ Streamlit Interactive UI

✔ Hugging Face Model Hosting

✔ GPU Training Support

✔ Model Comparison

✔ Confidence Score Prediction

✔ Production Ready

---

# 🧠 Problem Statement

Pneumonia is one of the leading causes of death worldwide.

Radiologists inspect Chest X-rays manually which is:

- Time Consuming
- Error Prone
- Expensive

This project automates the diagnosis process using Deep Learning.

---

# 📂 Dataset

## Chest X-Ray Dataset

```
Dataset
│
├── train
│      ├── NORMAL
│      └── PNEUMONIA
│
├── validation
│      ├── NORMAL
│      └── PNEUMONIA
│
└── test
       ├── NORMAL
       └── PNEUMONIA
```

Image Format

- JPG
- JPEG
- PNG

Classes

| Label | Description |
|---------|-------------|
| NORMAL | Healthy Lung |
| PNEUMONIA | Infected Lung |

---

# ⚙️ Tech Stack

## Programming

- Python

---

## Deep Learning

- TensorFlow
- Keras

---

## Computer Vision

- OpenCV
- NumPy
- Matplotlib

---

## Backend

- Flask

---

## Frontend

- Streamlit

---

## Deployment

- Hugging Face
- Render

---

## Version Control

- Git
- GitHub

---

# 🏗 Complete Project Architecture

```
                   Chest X-Ray Image
                           │
                           ▼
                  Upload Image (UI)
                           │
                           ▼
                Streamlit Frontend
                           │
                           ▼
                 Flask REST API
                           │
                           ▼
                 Image Preprocessing
                           │
        ┌──────────────────┼───────────────────┐
        │                  │                   │
        ▼                  ▼                   ▼
    CNN Model          CNN 20 Model       VGG16 Model
        │                  │                   │
        └──────────────────┼───────────────────┘
                           │
                           ▼
                 Prediction Probability
                           │
                           ▼
             NORMAL / PNEUMONIA Result
                           │
                           ▼
                  Display Prediction
```

---

# 📊 End-to-End Workflow

```
Dataset
    │
    ▼
Image Loading
    │
    ▼
Image Preprocessing
    │
    ▼
Resize Images (100x100)
    │
    ▼
Convert to Array
    │
    ▼
Normalize Images
    │
    ▼
Train / Validation Split
    │
    ▼
Model Training
    │
    ▼
Model Evaluation
    │
    ▼
Save Best Model
    │
    ▼
Deploy Model
    │
    ▼
User Upload Image
    │
    ▼
Prediction
```

---

# 🧹 Image Preprocessing Pipeline

Each X-Ray undergoes the following preprocessing steps:

### Step 1

Load Image

↓

### Step 2

Convert to Grayscale (CNN Models)

↓

### Step 3

Convert to RGB (VGG16)

↓

### Step 4

Resize

```
100 × 100
```

↓

### Step 5

Convert to NumPy Array

↓

### Step 6

Normalize Pixel Values

```
0 → 255

↓

0 → 1
```

↓

### Step 7

Feed into Model

---

# 🤖 Model 1

## Custom CNN (10 Epochs)

Architecture

```
Input (100×100×1)

↓

Conv2D (64)

↓

MaxPooling

↓

Dropout

↓

Conv2D (128)

↓

MaxPooling

↓

Dropout

↓

Conv2D (256)

↓

MaxPooling

↓

Dropout

↓

Flatten

↓

Dense (64)

↓

Dropout

↓

Dense (1)

↓

Sigmoid
```

Loss

```
Binary Crossentropy
```

Optimizer

```
Adam
```

Epochs

```
10
```

---

# 🤖 Model 2

## Improved CNN (20 Epochs)

Architecture

```
Input

↓

Conv2D (64)

↓

ReLU

↓

MaxPooling

↓

Dropout

↓

Conv2D (128)

↓

ReLU

↓

MaxPooling

↓

Dropout

↓

Conv2D (256)

↓

ReLU

↓

MaxPooling

↓

Dropout

↓

Flatten

↓

Dense (64)

↓

Dropout

↓

Dense (1)

↓

Sigmoid
```

Epochs

```
20
```

Optimizer

```
Adam
```

Loss

```
Binary Crossentropy
```

---

# 🤖 Model 3

## Transfer Learning (VGG16)

Pretrained

```
ImageNet
```

Frozen Layers

```
All VGG16 Convolution Layers
```

Custom Head

```
Flatten

↓

Dense (256)

↓

Dense (128)

↓

Dense (64)

↓

Dense (1)

↓

Sigmoid
```

Callbacks

- ModelCheckpoint
- EarlyStopping

---

# 🧠 Why VGG16?

Instead of training from scratch,

VGG16 already knows how to detect

- Edges
- Shapes
- Textures
- Patterns

Only the classifier is trained on Chest X-rays.

This greatly improves performance while reducing training time.

---

# 📁 Saved Models

```
model_xray.h5
```

Custom CNN

---

```
model_pre.h5
```

Improved CNN

---

```
best_model.keras
```

Best Transfer Learning Model

---

# 📈 Training Strategy

- GPU Training
- Batch Size = 4 (CNN)
- Batch Size = 32 (VGG16)
- Validation Dataset
- Binary Crossentropy
- Adam Optimizer
- Early Stopping
- Model Checkpoint

---

# 📊 Prediction Pipeline

```
Upload Image

↓

Read Image

↓

Resize

↓

Preprocess

↓

Load Model

↓

Predict Probability

↓

Threshold = 0.5

↓

NORMAL

or

PNEUMONIA
```

---

# 🌐 Deployment Architecture

```
                 User
                  │
                  ▼
          Streamlit Frontend
                  │
                  ▼
            Flask Backend
                  │
                  ▼
         Load Selected Model
                  │
      ┌───────────┼────────────┐
      ▼           ▼            ▼
   CNN10       CNN20       VGG16
      │           │            │
      └───────────┼────────────┘
                  ▼
          Prediction Engine
                  │
                  ▼
          Display Result
```

---

# 📂 Project Structure

```
Chest-XRay-Pneumonia-Detection
│
├── app.py
├── config.py
├── predictor.py
├── utils.py
├── requirements.txt
├── README.md
│
├── models
│     ├── model_xray.h5
│     ├── model_pre.h5
│     └── best_model.keras
│
├── static
│
├── templates
│
├── css
│
├── dataset
│
└── screenshots
```

---

# ▶️ Installation

Clone Repository

```bash
git clone https://github.com/yourusername/Chest-XRay-Pneumonia-Detection.git
```

Install Dependencies

```bash
pip install -r requirements.txt
```

Run Flask

```bash
python app.py
```

Run Streamlit

```bash
streamlit run app.py
```

---

# 🖥 Example Prediction

Input

```
Chest X-Ray Image
```

↓

Model Prediction

```
Probability : 0.9821

Prediction :

🦠 PNEUMONIA
```

---

# 📈 Future Improvements

- EfficientNet
- ResNet50
- DenseNet121
- Grad-CAM Heatmaps
- Multi-Class Disease Detection
- DICOM Support
- Cloud Deployment
- Docker
- CI/CD Pipeline
- REST API Authentication

---

# 👨‍💻 Author

**Sudheer Muthyala**

B.Tech – Electronics and Communication Engineering

Machine Learning | Deep Learning | Computer Vision | Python | Flask | Streamlit

GitHub:
https://github.com/M-Sudheer18

---

# ⭐ If you found this project helpful

Please consider giving this repository a ⭐ on GitHub.
It motivates future improvements and helps others discover the project.