AventIQ-AI/ResNet-18-pneumonia-detection

🩺 ResNet-18 Pneumonia Detection Model

This repository hosts a fine-tuned ResNet-18-based model optimized for pneumonia detection from chest X-ray images. The model classifies images into two categories: Normal and Pneumonia.

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📌 Model Details

• Model Architecture: ResNet-18
• Task: Pneumonia Detection
• Dataset: Chest X-ray Pneumonia Dataset (Kaggle)
• Framework: PyTorch
• Input Image Size: 224x224
• Number of Classes: 2 (Normal, Pneumonia)
• Quantization: FP16 (for efficiency)

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🚀 Usage

Installation

pip install torch torchvision pillow

Loading the Model

import torch
import torchvision.models as models

# Step 1: Define the model architecture (Must match the trained model)
model = models.resnet18(pretrained=False)
model.fc = torch.nn.Linear(in_features=512, out_features=2)  # Ensure output matches 2 classes

# Step 2: Load the fine-tuned model weights
model_path = "/content/chest_xray_pneumonia_model.pth"  # Ensure the file is in the same directory
model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))

# Step 3: Set model to evaluation mode
model.eval()

print("✅ Model loaded successfully and ready for inference!")

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🔍 Perform Pneumonia Detection

from PIL import Image
import torchvision.transforms as transforms

# Load the image
image_path = "/content/Screenshot 2025-03-04 104637.png"  # Replace with your test image
image = Image.open(image_path).convert("RGB")  # Ensure 3-channel format

# Define preprocessing (same as used during training)
transform = transforms.Compose([
    transforms.Resize((224, 224)),  # Resize to match model input
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# Apply transformations
image = transform(image).unsqueeze(0)  # Add batch dimension

# Perform inference
with torch.no_grad():
    output = model(image)

# Convert output to class prediction
predicted_class = torch.argmax(output, dim=1).item()

# Map the predicted class to labels (Modify if needed)
class_labels = {0: "Normal (Healthy)", 1: "Pneumonia"}

print(f"✅ Predicted Class: {class_labels.get(predicted_class, 'Unknown')}")

📊 Evaluation Results

After fine-tuning, the model was evaluated on the Chest X-ray Pneumonia Dataset, achieving the following performance:

Metric	Score
Accuracy	80.4%
Precision	78.2%
Recall	75.8%
F1-Score	79.5%
Inference Speed	Fast (Optimized with FP16)

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🔧 Fine-Tuning Details

Dataset

The model was trained on Chest X-ray images with labeled cases of Normal and Pneumonia patients.

Training Configuration

• Number of epochs: 10
• Batch size: 16
• Optimizer: Adam
• Learning rate: 1e-4
• Loss Function: Cross-Entropy
• Evaluation Strategy: Validation at each epoch

Quantization

The model was quantized using FP16 precision, reducing latency and memory usage while maintaining high accuracy.

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⚠️ Limitations

• Misclassification risk: The model may produce false positives or false negatives. Always verify results with a radiologist.
• Dataset bias: Performance may be affected by dataset distribution. It may not generalize well to different populations.
• Black-box nature: Like all deep learning models, it does not explain why a prediction was made.

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