A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task

Abstract

Background: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively trained with dermoscopic images in a clinical image classification task in direct competition with a large number of dermatologists has not been measured to date. This study compares the performance of a convolutional neuronal network trained with dermoscopic images exclusively for identifying melanoma in clinical photographs with the manual grading of the same images by dermatologists.

Artificial Intelligence in Dermatology: A Primer

“…Journal of Investigative Dermatology (2020), Volume 140 images nonetheless achieved dermatologist-level melanoma classification performance on nondermoscopic images (Brinker et al, 2019c).…”

Automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on whole tissue sections in breast carcinoma

“…Internationally, automatic analysis with the aid of artificial intelligence has covered a variety of diseases, ranging from "benign" conditions such as diabetic retinopathy and Alzheimer's disease [7], to malignant tumors such as breast cancer [26][27][28], lung cancer [29], liver cancer [30], skin cancer [31], osteosarcoma [32], and lymphoma [33,34], with an accuracy rate of 89.4-97.8%, and an AUC score of 0.85-0.94 [7,27,31]. In addition, various AI systems related to breast cancer have penetrated through different levels of IDC, such as histology-assisted and cytology-assisted diagnosis, mitotic cell count, lymph node metastasis assessment [9,10,18,22], breast cancer drug development and others [8], with an accuracy rate of 82.7-92.4% and an AUC score of 0.97 [27,28].…”

Review of medical image recognition technologies to detect melanomas using neural networks

“…By 2019, the accuracy of diagnostics using the ABCD algorithm and its additions has increased and, according to some data, has begun to exceed the results of dermatologists. In a study published in March 2019, the neural network had a smaller percentage of erroneous results compared to dermatologists (145 people), which indicates higher reliability of computer vision compared to human assessment for the tasks of classifying dermatological images [19].…”