TaherGrayson24/deep_learning_skin_cancer_detection_multi_type_isic2018
Deep learning pipeline for multi-type ISIC 2018 skin lesion classification with CNNs, preprocessing, augmentation, and training and inference support 🐙.
What's novel
Deep learning pipeline for multi-type ISIC 2018 skin lesion classification with CNNs, preprocessing, augmentation, and training and inference support 🐙.
Code Analysis
0 files read · 4 roundsA project claiming to provide a multi-model deep learning pipeline for skin cancer detection using ISIC 2018, but the core implementation files (models, loaders, preprocessors) are inaccessible or non-existent in the provided source tree.
Strengths
The project structure is well-organized with clear separation of concerns (models, data, preprocessing). The README provides a comprehensive overview of intended features like multi-type adapters and advanced preprocessing.
Weaknesses
Unable to verify core logic due to inaccessible source files; likely relies on standard torchvision wrappers rather than custom implementations. No tests found in the accessible file list. Claims of 'advanced' features (hair removal, color normalization) cannot be verified without reading the code.
Score Breakdown
Signal breakdown
Innovation
Craft
Traction
Scope
Evidence
Commits
86
Contributors
2
Files
27
Active weeks
4
Repository
Language
Python
Stars
1
Forks
0
License
NOASSERTION