README.md

metadata

license: cc-by-nc-4.0
datasets:
  - chest-xray-pneumonia
library_name: PyTorch
tags:
  - pneumonia-detection
  - cnn
  - medical-imaging
  - binary-classification
  - chest-xray
  - healthcare
  - pytorch
model-index:
  - name: ImprovedPneumoniaCNN
    results:
      - task:
          type: image-classification
          name: Pneumonia Detection
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9676
          - name: F1 Score
            type: f1
            value: 0.9685
          - name: AUC
            type: auc
            value: 0.9959
          - name: Loss
            type: loss
            value: 0.0778

🧠 ImprovedPneumoniaCNN: Pneumonia Detection from Chest X-rays

This repository hosts ImprovedPneumoniaCNN, a custom Convolutional Neural Network model designed to detect Pneumonia from chest X-ray images. It incorporates enhancements like dropout, batch normalization, SiLU activation, and Convolutional Block Attention Module (CBAM) for improved robustness and generalization.

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📊 Evaluation Results

Metric	Score
Accuracy	96.76%
F1 Score	0.9685
AUC	0.9959
Loss	0.0778

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Confusion Matrix

[[1680 42] [ 74 1782]]

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Classification Report

Class	Precision	Recall	F1-Score	Support
Normal	0.96	0.98	0.97	1722
Pneumonia	0.98	0.96	0.97	1856

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🏗️ Architecture Highlights

• Custom CNN with residual blocks
• Uses CBAM attention for spatial and channel refinement
• SiLU activation for better non-linearity
• Dropout and BatchNorm for regularization
• Final Global Average Pooling + FC layer

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🚀 How to Use

🔧 Install Dependencies

pip install torch torchvision albumentations scikit-learn matplotlib seaborn
import torch
from torchvision import transforms
from PIL import Image
from model import ImprovedPneumoniaCNN  # make sure model is defined/imported

# Load model
model = ImprovedPneumoniaCNN()
model.load_state_dict(torch.load("improved_pneumonia_cnn.pth", map_location=torch.device('cpu')))
model.eval()

# Preprocess image
transform = transforms.Compose([
    transforms.Grayscale(),
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
])

img = Image.open("path_to_chest_xray.jpg")
img_tensor = transform(img).unsqueeze(0)

# Predict
with torch.no_grad():
    output = model(img_tensor)
    prediction = torch.sigmoid(output).item()
    print("Pneumonia" if prediction > 0.5 else "Normal")

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Contributors

• Thiyaga158

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License

This model is licensed under CC BY-NC 3.0.
For research and educational use only.

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