Skin Cancer Detection Using Lightweight Deep Learning Models
DOI:
https://doi.org/10.48047/2w3k6x04Keywords:
Convolutional Neural Networks, Deep Learning, Machine Learning, Skin Cancer Detection.Abstract
Skin cancer is one of the most prevalent cancers in the world with an increase in the number of cases each year. Early detection significantly improves the prognosis for malignant melanoma patients but traditional diagnosis methods are time-consuming and invasive. This paper explores how lightweight deep learning models like MobileNetV2, NAS Net Mobile, ResNet50, and EfficientNetB0 can aid in the early and accurate detection of skin cancer.
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References
N. Hasan et al., “Skin cancer: understanding the journey of transformation from conventional to advanced treatment approaches,” Mol. Cancer, vol. 22, no. 1, p. 168, Oct. 2023, doi: 10.1186/s12943-023-01854-3.
S. An et al., “Indoor Tanning and the Risk of Overall and Early-Onset Melanoma and Non-Melanoma Skin Cancer: Systematic Review and Meta-Analysis,” Cancers (Basel)., vol. 13, no. 23, p. 5940, Nov. 2021, doi: 10.3390/cancers13235940.
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