Datasets:
ArXiv:
License:
| { | |
| "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 | |
| } |