GiovanniIacuzzo/Esame-Machine-Learning
Medical image classification of skin lesions using PyTorch and scikit-learn. Combines a Deep Learning pipeline (Convolutional Autoencoder + neural classifier) with a hybrid approach leveraging Vision Transformer embeddings and XGBoost for robust and efficient model comparison.
This project helps medical professionals, researchers, or students classify skin lesions from dermatoscopic images. It takes an image of a skin lesion and processes it through two different machine learning models to determine its classification. The output is a classification of the lesion, allowing for comparison of model performance.
No commits in the last 6 months.
Use this if you need to classify skin lesions from dermatoscopic images and want to compare the effectiveness of deep learning and hybrid machine learning approaches.
Not ideal if you need a pre-built, production-ready diagnostic tool without any machine learning setup or if you are not working with skin lesion images.
Stars
12
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1
Language
Python
License
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Category
Last pushed
Sep 06, 2025
Commits (30d)
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