| --- |
| 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 |
| |
