README.md

metadata

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
• For binary EfficientNet-B3 classifiers Pneumonia Multi-Class Dataset

Interactive Gradio Web App

• check out the live web app click here
• For Models click here

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:

ROC Curve:

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:

ROC Curve:

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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