Melanoma is a malignant cancer type which develops when DNA damage occurs (mainly due to environmental factors such as ultraviolet rays). Often, melanoma results in intense and aggressive cell growth that, if not caught in time, can bring one toward death. Thus, early identification at the initial stage is fundamental to stopping the spread of cancer. In this paper, a ViT-based architecture able to classify melanoma versus non-cancerous lesions is presented. The proposed predictive model is trained and tested on public skin cancer data from the ISIC challenge, and the obtained results are highly promising. Different classifier configurations are considered and analyzed in order to find the most discriminating one. The best one reached an accuracy of 0.948, sensitivity of 0.928, specificity of 0.967, and AUROC of 0.948.

Cirrincione, G., Cannata, S., Cicceri, G., Prinzi, F., Currieri, T., Lovino, M., et al. (2023). Transformer-Based Approach to Melanoma Detection. SENSORS, 23(12), 1-13 [10.3390/s23125677].

Transformer-Based Approach to Melanoma Detection

Cicceri, Giovanni;Prinzi, Francesco;Currieri, Tiziana;Vitabile, Salvatore
2023-06-17

Abstract

Melanoma is a malignant cancer type which develops when DNA damage occurs (mainly due to environmental factors such as ultraviolet rays). Often, melanoma results in intense and aggressive cell growth that, if not caught in time, can bring one toward death. Thus, early identification at the initial stage is fundamental to stopping the spread of cancer. In this paper, a ViT-based architecture able to classify melanoma versus non-cancerous lesions is presented. The proposed predictive model is trained and tested on public skin cancer data from the ISIC challenge, and the obtained results are highly promising. Different classifier configurations are considered and analyzed in order to find the most discriminating one. The best one reached an accuracy of 0.948, sensitivity of 0.928, specificity of 0.967, and AUROC of 0.948.
17-giu-2023
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10304033/
Cirrincione, G., Cannata, S., Cicceri, G., Prinzi, F., Currieri, T., Lovino, M., et al. (2023). Transformer-Based Approach to Melanoma Detection. SENSORS, 23(12), 1-13 [10.3390/s23125677].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/620087
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