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{
"name": "42_Medical_Image_Classification_DenseNet121_ChestXray_DL",
"query": "Create a medical image classification system using a pre-trained DenseNet-121 model and the Kaggle Chest X-ray dataset. Start by loading and preprocessing the dataset and performing data augmentation (including rotation, translation, and scaling) in `src/data_loader.py`. Apply the DenseNet-121 model for classification, recording the accuracy and saving it to `results/metrics/classification_accuracy.txt`. Fine-tune the model and save it as `models/saved_models/chest_xray_densenet_model.pth`. Use Grad-CAM to visualize the model's decision-making process and save these visualizations as `results/figures/grad_cam_visualizations.gif`. Finally, create a Markdown report that documents the model architecture, training process, data augmentation techniques, and analysis of the results, and save it as `results/medical_image_classification_report.md`. It would also be nice if the system was flexible such that the DenseNet-121 could be easily further fine-tuned by a human user.",
"tags": [
"Classification",
"Computer Vision",
"Medical Analysis",
"Supervised Learning"
],
"requirements": [
{
"requirement_id": 0,
"prerequisites": [],
"criteria": "The \"Kaggle Chest X-ray\" dataset is used, with data loading and preprocessing implemented in `src/data_loader.py`.",
"category": "Dataset or Environment",
"satisfied": null
},
{
"requirement_id": 1,
"prerequisites": [
0
],
"criteria": "Data augmentation is performed, including rotation, translation, and scaling of images in `src/data_loader.py`.",
"category": "Data preprocessing and postprocessing",
"satisfied": null
},
{
"requirement_id": 2,
"prerequisites": [
1
],
"criteria": "The pre-trained \"DenseNet-121\" model is fine-tuned saved in `models/saved_models/`.",
"category": "Machine Learning Method",
"satisfied": null
},
{
"requirement_id": 3,
"prerequisites": [
1,
2
],
"criteria": "Classification accuracy is printed and saved as `results/metrics/classification_accuracy.txt`.",
"category": "Performance Metrics",
"satisfied": null
},
{
"requirement_id": 4,
"prerequisites": [
2,
3
],
"criteria": "\"Grad-CAM\" is used to visualize model decisions, saving the visualizations as `results/figures/grad_cam_visualizations.gif`.",
"category": "Visualization",
"satisfied": null
},
{
"requirement_id": 5,
"prerequisites": [
2,
3
],
"criteria": "A \"Markdown\" report is created containing the model architecture, training process, data augmentation, and result analysis, and saved as `results/medical_image_classification_report.md`.",
"category": "Other",
"satisfied": null
}
],
"preferences": [
{
"preference_id": 0,
"criteria": "The \"Markdown\" report should include a section explaining the impact of data augmentation on model performance.",
"satisfied": null
},
{
"preference_id": 1,
"criteria": "The \"Grad-CAM\" visualizations should clearly highlight the areas of the images that contributed most to the model's decisions.",
"satisfied": null
},
{
"preference_id": 2,
"criteria": "The system should be flexible to allow further fine-tuning of the \"DenseNet-121\" model.",
"satisfied": null
}
],
"is_kaggle_api_needed": true,
"is_training_needed": true,
"is_web_navigation_needed": false
} |