README.md

---
title: Classification With Segmentation
emoji: 🏢
colorFrom: red
colorTo: red
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false
license: mit
---

# 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.

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## 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

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## 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.

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## 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.

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## 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">

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## ⚠️ 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.

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## License
MIT License