Summary of Skin Cancer Images Classification Using Transfer Learning Techniques, by Md Sirajul Islam et al.
Skin Cancer Images Classification using Transfer Learning Techniques
by Md Sirajul Islam, Sanjeev Panta
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper aims to develop a reliable automated system for early skin cancer diagnosis using deep learning and transfer learning models. Building upon previous studies, the authors fine-tune five different pre-trained approaches for binary classification of benign and malignant skin cancer stages. The ISIC dataset is used for evaluation, with data augmentation techniques applied to enhance model stability. Experimental results show that the ResNet-50 model achieves an accuracy of 0.935, F1-score of 0.86, and precision of 0.94. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to create a machine that can diagnose skin cancer really early on. This is important because it could save many people’s lives. Scientists have already tried using deep learning and transfer learning models to do this, but their results haven’t been great. So the authors of this study try five different approaches and make them work better by adjusting certain parts. They use a big dataset to test these methods and find that one of them, called ResNet-50, does really well. |
Keywords
» Artificial intelligence » Classification » Data augmentation » Deep learning » F1 score » Precision » Resnet » Transfer learning
