ResNet-50 for Pneumonia Detection

This model is a fine-tuned version of microsoft/resnet-50 trained to classify chest X-ray images into two categories: NORMAL and PNEUMONIA.

It was trained to handle class imbalances using weighted Cross-Entropy loss and utilizes specific image augmentations suited for medical radiography.

Dataset

The model was trained on the Chest X-Ray Images (Pneumonia) dataset.

• Normal: 1,341 training images
• Pneumonia: 3,876 training images

Training Procedure

Preprocessing and Augmentation

Images were resized and normalized using the AutoImageProcessor configuration from the base ResNet-50 model. During training, the following augmentations were applied to improve generalization:

• Random Resized Crop
• Random Horizontal Flip
• Random Rotation (15 degrees)
• Color Jitter (Brightness and Contrast)

Hyperparameters

• Learning Rate: 5e-5
• Train Batch Size: 8
• Gradient Accumulation Steps: 8 (Effective Batch Size: 64)
• Eval Batch Size: 8
• Epochs: 5
• Warmup Ratio: 0.1
• Loss Function: Weighted Cross-Entropy Loss (to penalize minority class misclassifications)
• Optimization Strategy: Best model loaded at the end based on F1 score.

Evaluation Results

On the standard test split, the model achieved the following performance:

• Accuracy: 0.833
• F1 Score (Weighted): 0.835