Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities.

TL;DR: Dermoscopic images obtained from digital single-lens reflex (DSLR) cameras, smartphones and a lightweight USB camera are compared using artificial intelligence (AI) algorithm for determining the accuracy of melanoma identification.

TL;DR: A novel deep learning-based skin cancer detector using an imbalanced dataset was proposed and RegNetY-320 outperformed InceptionV3 and AlexNet in terms of the accuracy, F1-score, and receiver operating characteristic (ROC) curve both on the imbalanced and balanced datasets.

TL;DR: In this article, the authors reviewed the available information on the early diagnosis and management of melanoma and discussed such information's potential in facilitating the future prospective, which is expected to be particularly useful in the early detection of possible metastatic lesions and the development of new therapeutic approaches.

TL;DR: A critical assessment of machine learning and deep learning models for the classification of skin tumors finds that pre-trained models perform extremely well for skin tumor classification, and metrics, such as the F-score and accuracy, indicate that this is most notably the case for VGG16.

TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.

TL;DR: A status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions.

TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.

TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.