E03_ResNet18_Scheduler_Chest_XRay

E03 ResNet18 Scheduler - Chest X-Ray Classification

Overview

This project develops a deep learning image classification model for classifying chest X-ray images into two classes:

• NORMAL
• PNEUMONIA

The model uses a pretrained ResNet18 architecture with transfer learning and a StepLR learning-rate scheduler.

This project was developed as part of an AI Engineering Bootcamp final portfolio project.

Model

• Architecture: ResNet18
• Framework: PyTorch
• Pretrained weights: ImageNet
• Number of classes: 2
• Optimizer: Adam
• Initial learning rate: 0.0001
• Batch size: 32
• Epochs: 5
• Loss function: CrossEntropyLoss with class weights
• Scheduler: StepLR
• Scheduler step size: 2
• Scheduler gamma: 0.1

Classes

1. NORMAL
2. PNEUMONIA

Test Results

The final E03 model achieved:

Classification Report

Confusion Matrix

Actual NORMAL: 192 NORMAL, 42 PNEUMONIA

Actual PNEUMONIA: 6 NORMAL, 384 PNEUMONIA

Methodology

The model was developed using transfer learning with a pretrained ResNet18 network. The final fully connected layer was replaced with a two-class classification layer.

The training process used:

1. Image preprocessing and dataset preparation
2. Train/validation/test split
3. ResNet18 pretrained on ImageNet
4. Weighted cross-entropy loss
5. Adam optimizer
6. StepLR learning-rate scheduling
7. Five training epochs
8. Best-model selection based on validation loss
9. Evaluation on a held-out test set

Intended Use

This model is intended for educational and experimental purposes, particularly for demonstrating computer vision and transfer learning workflows.

It should NOT be used as a medical diagnostic system or as a substitute for professional medical assessment.

Limitations

The model was trained and evaluated on a specific chest X-ray dataset. Its performance may not generalize to images from different hospitals, devices, patient populations, or acquisition protocols.

The model should therefore not be used for clinical decision-making without appropriate external validation, clinical testing, regulatory review, and professional oversight.

Model File

The trained model weights are provided in:

E03_ResNet18_Scheduler_best.pth

Author

Bunsya

AI Engineering Bootcamp - Final Portfolio Project