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+# 🩺 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.