Skin Cancer Detection using VGG-16
1 Department of Computer Science and Engineering, Khulna University of Engineering and Technology (KUET), Khulna, Bangladesh.
2 Department of Statistics and Data Science, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh.
3 Centre for Smart Analytics, Federation University Australia, Victoria, Australia.
Research Article
World Journal of Advanced Research and Reviews, 2024, 24(02), 2930-2937
Article DOI: 10.30574/wjarr.2024.24.2.3520
Publication history:
Received on 07 October 2024; revised on 23 November 2024; accepted on 28 November 2024
Abstract:
The recent increase in the prevalence of skin cancer, along with its significant impact on individuals’ lives, has garnered the attention of many researchers in the field of deep learning models, especially following the promising results observed using these models in the medical field. This study aimed to develop a system that can accurately diagnose one of three types of skin cancer: basal cell carcinoma (BCC), melanoma (MEL), and nevi (NV). Additionally, it emphasizes the importance of image quality, as many studies focus on the quantity of images used in deep learning. In this study, transfer learning was employed using the pre-trained VGG-16 model alongside a dataset sourced from Kaggle.
Keywords:
Skin cancer; Deep learning; Basal cell carcinoma (BCC); Melanoma (MEL); Nevi (NV), Dermoscopic images; VGG-16
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0