Abstract
Skin cancer is one of the worst diseases noticed in humankind. It beholds some types, which even experts find challenging to categorize. In recent times, neural network-based automated systems have been entitled to perform this difficult task for their amazing ability of pattern recognition. However, the challenge remains due to the requirement for high-quality images and thus the necessity of highly configured resources. In this research manuscript, the authors have addressed these issues. They pushed the boundary of neural networks by utilizing a low-resolution (80 × 80, 64 × 64, and 32 × 32 pixels), highly imbalanced, grayscale HAM10000 skin cancer dataset into several pre-trained network architectures (VGG16, DenseNet169, DenseNet161, and ResNet50) that have been successfully used for a similar purpose with a high-resolution, augmented RGB HAM10000 skin cancer image dataset. The image resolution of the original HAM10000 dataset is 800 × 600 pixels. The highest achieved performance for 80 × 80, 64 × 64, and 32 × 32 pixel images were 80.46%, 78.56%, and 74.15%, respectively. All of these results were accomplished from the ImageNet pre-trained VGG16 model. The second-best model in terms of transfer learning was DenseNet169. The performances demonstrate that even within these severe circumstances, neural network-based transfer learning holds promising possibilities.
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Khan, M.D.R.H., Uddin, A.H., Nahid, AA., Bairagi, A.K. (2022). Skin Cancer Detection from Low-Resolution Images Using Transfer Learning. In: Raj, J.S., Palanisamy, R., Perikos, I., Shi, Y. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 213. Springer, Singapore. https://doi.org/10.1007/978-981-16-2422-3_26
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DOI: https://doi.org/10.1007/978-981-16-2422-3_26
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