Update README.md

README.md

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 license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Classification With Segmentation 
+
+This project implements a complete chest X-ray analysis pipeline using:
+
+**lung segmentation using UNet (Unet.ipynb)**
+
+**Two binary EfficientNet-B3 classifiers (pneumonia classification.ipynb):**
+
+- Normal vs Bacterial Pneumonia
+- Normal vs Viral Pneumonia
+
+**Intelligent decision fusion**
+
+- If both classifiers fire, the model selects the dominant probability and returns a final diagnosis.
+
+---
+## Dataset
+- For Unet [Lung Mask Image Dataset](https://www.kaggle.com/datasets/newra008/lung-mask-image-dataset)
+- For binary EfficientNet-B3 classifiers [Pneumonia Multi-Class Dataset ](https://www.kaggle.com/datasets/kmljts/pneumonia-multiclass)
+## Interactive Gradio Web App
+- check out the live web app click [here](https://huggingface.co/spaces/Clocksp/classification_with_segmentation)
+- For Models click [here](https://huggingface.co/spaces/Clocksp/classification_with_segmentation/tree/main)
+### Includes
+- Lung segmentation mask
+- Masked classifier input
+- Bacterial & viral probability scores
+- Final diagnosis
+- Segmentation overlay
+- Static Test Samples Table at the bottom
+
+---
+## Features
+
+### 1. Lung Segmentation (UNet)
+The UNet model extracts lung regions from chest X-rays, removing background and irrelevant anatomical structures.
+
+### 2. Masked Image Classification
+Two EfficientNet-B3 classifiers operate only on segmented lung regions, leading to improved diagnostic accuracy.
+
+
+### 3. Dual-Model Decision Logic
+
+| Condition                     | Final Output             |
+|------------------------------|--------------------------|
+| Both bacterial & viral probabilities are low | **NORMAL**              |
+| Bacterial probability high only | **BACTERIAL PNEUMONIA** |
+| Viral probability high only     | **VIRAL PNEUMONIA**     |
+| Both probabilities high         | Chooses the **higher** probability |
+
+### 4. Important Note on Viral vs Bacterial Overlap
+
+Chest X-ray images of viral and bacterial pneumonia often look very similar, with overlapping radiological patterns such as:
+
+- Diffuse opacities
+- Patchy consolidations
+- Similar lung distribution
+
+Because of this natural similarity, perfect separation between viral and bacterial pneumonia is not guaranteed, and classifiers may show overlapping confidence values. A fallback rule chooses the class with the higher probability when both are high.
+
+---
+
+## Model Details
+
+### UNet
+- **Input:** 300 × 300  
+- **Output:** Lung segmentation mask  
+- **Purpose:** Improves classifier performance by isolating lung regions and removing irrelevant anatomy.
+
+
+### EfficientNet-B3 Classifiers
+Two separate binary classifiers are used:
+
+- **Normal vs Bacterial**
+- **Normal vs Viral**
+
+This modular setup allows each pneumonia type to be diagnosed independently and increases robustness.
+
+---
+
+## Performance Metrics
+
+### 1. UNet Lung Segmentation
+The segmentation model achieves high spatial accuracy, ensuring downstream classifiers receive clean, lung-focused inputs.
+
+| Metric                       | Score   |
+|------------------------------|---------|
+| Accuracy                     | 0.9899  |
+| Precision                    | 0.9828  |
+| Recall                       | 0.9749  |
+| F1 Score                     | 0.9788  |
+| IoU (Intersection over Union) | 0.9585 |
+| Dice Coefficient             | 0.9788  |
+
+
+### 2. Normal vs Bacterial Pneumonia Classifier (EfficientNet-B3)
+
+#### Class-wise Performance
+| Class         | Precision | Recall | F1-Score | Support |
+|---------------|-----------|--------|----------|---------|
+| Normal (0)    | 0.98      | 0.97   | 0.97     | 475     |
+| Bacterial (1) | 0.98      | 0.99   | 0.99     | 834     |
+
+#### Overall Metrics
+| Metric        | Score |
+|---------------|-------|
+| Accuracy      | 0.98  |
+| Macro Avg F1  | 0.98  |
+| Weighted Avg F1 | 0.98 |
+
+**Confusion Matrix:**
+
+<img src="Assests/c1.png">
+
+**ROC Curve:**
+<img src="Assests/r1.png">
+
+### 3. Normal vs Viral Pneumonia Classifier (EfficientNet-B3)
+
+#### Class-wise Performance
+| Class         | Precision | Recall | F1-Score | Support |
+|---------------|-----------|--------|----------|---------|
+| Normal (0)    | 0.99      | 0.99   | 0.99     | 475     |
+| Viral (1)     | 0.99      | 0.99   | 0.99     | 448     |
+
+#### Overall Metrics
+| Metric        | Score |
+|---------------|-------|
+| Accuracy      | 0.99  |
+| Macro Avg F1  | 0.99  |
+| Weighted Avg F1 | 0.99 |
+
+**Confusion Matrix:**
+<img src="Assests/c2.png">
+
+**ROC Curve:**
+<img src="Assests/r2.png">
+
+---
+## ⚠️ Important Interpretation Note
+
+Despite strong quantitative performance, viral and bacterial pneumonia can present with highly overlapping radiological features, making them difficult to distinguish purely from chest X-ray images — even for trained radiologists.
+
+**Therefore:**
+
+- Some misclassification between **Viral ↔ Bacterial** is expected.  
+- The system applies a **fallback decision rule** when both probabilities are high.  
+- Outputs should **not** be used for any form of clinical diagnosis or medical decision-making.
+
+--- 
+
+## License
+MIT License