---
library_name: pytorch
tags:
- chest-xray
- image-classification
- pneumonia
- resnet18
- computer-vision
- medical-imaging
license: mit
---

# 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:

| Metric | Score |
|---|---:|
| Test Accuracy | 92.31% |
| Weighted F1-score | 92.16% |
| Macro F1-score | 91.50% |

### Classification Report

| Class | Precision | Recall | F1-score |
|---|---:|---:|---:|
| NORMAL | 0.9697 | 0.8205 | 0.8889 |
| PNEUMONIA | 0.9014 | 0.9846 | 0.9412 |

### 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