Background: State-of-the-art classifiers based on convolutional neural networks (CNNs)
were shown to classify images of skin cancer on par with dermatologists and could enable …

Background: Skin cancer classificationusing convolutional neural networks (CNNs) proved
better results in classifying skin lesions compared with dermatologists which is lifesaving in …

Background Recent years have been witnessing a substantial improvement in the accuracy
of skin cancer classification using convolutional neural networks (CNNs). CNNs perform on …

Skin cancer (SC) is one of the most prevalent cancers worldwide. Clinical evaluation of skin
lesions is necessary to assess the characteristics of the disease; however, it is limited by …

Artificial intelligence (AI) based on machine learning and convolutional neuron networks
(CNN) is rapidly becoming a realistic prospect in dermatology. Non-melanoma skin cancer …

Skin cancer is one of the major types of cancers with an increasing incidence over the past
decades. Accurately diagnosing skin lesions to discriminate between benign and malignant …

Background Multiple studies have compared the performance of artificial intelligence (AI)–
based models for automated skin cancer classification to human experts, thus setting the …

Background Skin cancer is the most common cancer type affecting humans. Traditional skin
cancer diagnosis methods are costly, require a professional physician, and take time …

Background The diagnostic performance of convolutional neural networks (CNNs) for
diagnosing several types of skin neoplasms has been demonstrated as comparable with …

Detecting dangerous illnesses connected to the skin organ, particularly malignancy,
requires the identification of pigmented skin lesions. Image detection techniques and …