SkinGuard receives the “Best Healthcare Solution Award” at the IOT solutions World Congress

SkinGuard is a computer-vision smartphone-based application utilising cutting-edge machine learning technologies to identify malignant skin lesions with an accuracy above 96%. It was fully developed and implemented by Topazium and it is intended for easy detection of skin cancer like melanoma to help practitioners (especially GPs) identifying the disease in its early stage. SkinGuardTM focuses…

Prospective evaluation of SkinGuard, a deep algorithmic framework for the classification of neoplastic skin lesions

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…

Deep Neural Frameworks Improve the Accuracy of General Practitioners in the Classification of Pigmented Skin Lesions

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…

Deep neural frameworks improve the accuracy of general practitioners in the classification of pigmented skin lesions

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…