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Optimized ResNet-50 CNN for Smart Healthcare Skin Cancer Classification
Authors
1 Professor, Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bengaluru, Karnataka, India. 2 3 4 Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bengaluru, Karnataka, India.
Article Information
- Published In
- Volume 6, Issue 6 (2025)
- Published Online
- November-December 2025
- Pages
- 130-139
- Article Type
- Original Article
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References
1. Tschandl P., et al., “The HAM10000 dataset Large number of multi-source dermatoscopes available images,” Scientific Data,
2. Selvaraju R. R. **et al.**, “Grad-CAM: Visual explanations from deep networks,” **IEEE ICCV
3. Esteva A., et al. “Dermatologist- classification of skin cancer with deep neural networks.” Nature, 2017.
4. He K., etal. “Deep Residual Learning for Image Recognition.” IEEE CVPR, 2016.
5. Simonyan K., Zisserman A., “Very deep convolutional networks for large scale image recognition,” arXiv, 2014.
2. Selvaraju R. R. **et al.**, “Grad-CAM: Visual explanations from deep networks,” **IEEE ICCV
3. Esteva A., et al. “Dermatologist- classification of skin cancer with deep neural networks.” Nature, 2017.
4. He K., etal. “Deep Residual Learning for Image Recognition.” IEEE CVPR, 2016.
5. Simonyan K., Zisserman A., “Very deep convolutional networks for large scale image recognition,” arXiv, 2014.
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