According to data from the Argentinean Society of Dermatology (SAD), 40% of Argentines claim to protect themselves from the sun occasionally and more than 60% do not renew sun protection during the day. Summer and sun exposure at the beach increase the risk of skin cancer and for this reason early detection of changes in…
Introduction. Diagnostic errors between nevic lesions and skin tumors are frequent for non-specialist physicians. Therefore, there is a need to create a simple and practical tool based on artificial intelligence to assists them in distinguishing potentially malignant lesions from benign ones, improving early detection of skin cancer. Objective. To evaluate the performance of SkinGuard, an…
This study evaluated whether deep learning frameworks trained in large datasets can help non-dermatologist physicians improve their accuracy in categorizing the seven most common pigmented skin lesions. Open-source skin images were downloaded from the International Skin Imaging Collaboration (ISIC) archive. Different deep neural networks (DNNs) (n = 8) were trained based on a random dataset…
Artificial intelligence can be a key tool in the context of assisting in the diagnosis of dermatological conditions, particularly when performed by general practitioners with limited or no access to high resolution optical equipment. This study evaluates the performance of deep convolutional neural networks (DNNs) in the classification of seven pigmented skin lesions. Additionally, it…
Skin lesions are one of the most common human diseases and affect millions of people worldwide 1. Moreover, its prevalence will raise every year due to the aging of the population; studies reveal that it increases to 50% in patients over 65 years, with an average of more than two skin diseases per elder 2.…