Application of Deep Learning to Detect Melanoma on Dermoscopy Images

Melanoma is a type of skin cancer that affects many people worldwide. Although it only comprises 1% of the total skin cancer incidence, 80% of deaths due to skin cancer is related to melanoma. Dermatologists are healthcare specialists that have a big role in the diagnosis of melanoma. However, these experts are not sufficient enough to cover a wide population. To augment this problem, creation of MelanomaENV2 is proposed. This research used the EfficientNet V2, and its variants, as template to create MelanomaENV2 model that can predict melanoma based on dermoscopy images. It is trained in 919 images: 400 for melanoma and 519 for nonmelanoma, which were sampled from the Human Against Machine dataset (HAM10000). Among the variants, efficientnetv2-l-21k-ft1k has the highest overall score among the evaluation metrics: 0.73 in precision, 0.69 in recall, 0.71 in F1 score, 0.88 in training accuracy, 0.72 in test accuracy, and 0.72 in Area Under the Receiver Operating Characteristic Curve (AUC-ROC).