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Original Article
Optimized ResNet-50 CNN for Smart Healthcare Skin Cancer Classification
Yashaswini H R1
Jyothi B G2
Navyashree S3
Punyakala K L4
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.
Published Online: November-December 2025
Pages: 130-139
Cite this article
↗ https://www.doi.org/10.59256/ijire.20250606020
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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