https://github.com/achraf-oujjir/xception-on-ham10k

In this project, we used a transfer learning approach to build an image classification model for the classification of skin lesion, we trained our model specifically on the ham10000 dataset available on kaggle and we were able to achieve a 93.6% accuracy
https://github.com/achraf-oujjir/xception-on-ham10k

cnn computer-vision data-science deep-learning ham10000 jupyter-notebook kaggle python skin-lesion-classification transfer-learning xception-net

Last synced: 3 months ago
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In this project, we used a transfer learning approach to build an image classification model for the classification of skin lesion, we trained our model specifically on the ham10000 dataset available on kaggle and we were able to achieve a 93.6% accuracy

• Host: GitHub
• URL: https://github.com/achraf-oujjir/xception-on-ham10k
• Owner: achraf-oujjir
• Created: 2024-01-15T19:22:10.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-01-15T19:25:42.000Z (over 1 year ago)
• Last Synced: 2025-01-08T23:25:58.046Z (5 months ago)
• Topics: cnn, computer-vision, data-science, deep-learning, ham10000, jupyter-notebook, kaggle, python, skin-lesion-classification, transfer-learning, xception-net
• Language: Jupyter Notebook
• Homepage:
• Size: 7.81 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# xception-on-ham10k

In this project, we used a transfer learning approach to build an image classification model for the classification of skin lesion, we trained our model specifically on the ham10000 dataset available on kaggle and we were able to achieve a 93.6% accuracy. This is a project that I worked on with Hamza JAKOUK and Leandre PACIS.