1 Department of Electrical and Electronic Engineering, University of Rajshahi, Rajshahi, Bangladesh.
2 Department of Electrical and Electronic Engineering, Leading University, Sylhet, Bangladesh.
3 Department of Electrical and Electronic Engineering, Southern University Bangladesh, Chattogram, Bangladesh.
4 Department of EEE, Daffodil International University, Daffodil Smart City (DSC), Dhaka-1216, Dhaka, Bangladesh.
5 Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.
Research Article
World Journal of Advanced Research and Reviews, 2023, 19(01), 1652-1658
Received on 02 June 2023; revised on 17 July 2023; accepted on 26 July 2023
Skin cancer remains one of the major causes of mortality worldwide, with malignant melanoma being the deadliest type due to its high potential for metastasis. Although relatively uncommon, it accounts for nearly 75% of skin cancer-related deaths. Early detection plays a crucial role in improving outcomes, but it is difficult because melanoma often closely resembles benign skin lesions. In this work, we propose an automated system for early melanoma detection using deep transfer learning. Our method leverages a pre-trained NASNet model, from which features are transferred to a new dataset for classification. We adapted the original network by incorporating global average pooling and customized classification layers. The system was trained and evaluated on skin images from the ISIC 2020 dataset.
Skin cancer; Deep learning; Melanoma detection; Dermoscopic images; NASNet
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Md Masum Billah, Amit Deb Nath, Denesh Das, Tanvir Mahmud and Rashedur Rahman. Skin Cancer Classification using NasNet. World Journal of Advanced Research and Reviews, 2023, 19(1), 1652-1658. Article DOI: https://doi.org/10.30574/wjarr.2023.19.1.1336