Cutaneous melanoma (CM) is potentially the most dangerous form of skin tumor and causes
90% of skin cancer mortality. A unique collaboration of multi-disciplinary experts from the …

Abstract The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion
analysis is an emerging field of research that has the potential to alleviate the burden and …

We investigated whether human preferences hold the potential to improve diagnostic
artificial intelligence (AI)-based decision support using skin cancer diagnosis as a use case …

Automatic melanoma detection from dermoscopic skin samples is a very challenging task.
However, using a deep learning approach as a machine vision tool can overcome some …

Background Due to their ability to solve complex problems, deep neural networks (DNNs)
are becoming increasingly popular in medical applications. However, decision-making by …

We carry out a critical assessment of machine learning and deep learning models for the
classification of skin tumors. Machine learning (ML) algorithms tested in this work include …

Simple Summary The proposed research aims to provide a deep insight into the deep
learning and machine learning techniques used for diagnosing skin cancer. While …

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 most dangerous diseases in the world. Correctly classifying skin
lesions at an early stage could aid clinical decision-making by providing an accurate …

Melanoma is the most fatal type of skin cancer which can cause the death of victims at the
advanced stage. Extensive work has been presented by the researcher on computer vision …