Abstract
Skin cancer is one of the major causes of death worldwide. It can be screened early by identifying certain types of skin lesion that correlates to the possibility of developing into full cancer cells. It is a difficult task to distinguish between multiple types of the lesion as their appearance do not differ that much. Thus, a modified DenseNet, which is based on convolutional neural networks has been proposed to identify three types of skin lesions that are related to skin cancer. A network derived from DenseNet-264 is used as the base network, in which an atrous spatial pyramid pooling unit is integrated to improve the network capability in handling multi-scale detection. The simulation results show that the improved network has successfully identified 84.43% of the skin lesion conditions.
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Acknowledgments
The authors would like to acknowledge funding from Ministry of Higher Education Malaysia (Fundamental Research Grant Scheme: FRGS/1/2015/TK04/UKM/01/3) and Universiti Kebangsaan Malaysia (Geran Universiti Penyelidikan: GUP-2015–053).
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Stofa, M.M., Zulkifley, M.A., Zainuri, M.A.A.M., Moubark, A.M. (2022). DenseNet with Atrous Spatial Pyramid Pooling for Skin Lesion Classification. In: Mahyuddin, N.M., Mat Noor, N.R., Mat Sakim, H.A. (eds) Proceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol 829. Springer, Singapore. https://doi.org/10.1007/978-981-16-8129-5_126
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DOI: https://doi.org/10.1007/978-981-16-8129-5_126
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