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Nevus, Pigmented

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Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined naevi and melanomas.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: Deep learning convolutional neural networks (CNN) may assist physicians in the diagnosis of melanoma. The capacity of a CNN to differentiate melanomas from combined naevi, the latter representing well-known melanoma simulators, has not be...

[Clinical image identification of basal cell carcinoma and pigmented nevi based on convolutional neural network].

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences
To construct an intelligent assistant diagnosis model based on the clinical images of basal cell carcinoma (BCC) and pigmented nevi in Chinese by using the advanced convolutional neural network (CNN).
 Methods: Based on the Xiangya Medical Big Data P...

Prediction of melanoma evolution in melanocytic nevi via artificial intelligence: A call for prospective data.

European journal of cancer (Oxford, England : 1990)
Recent research revealed the superiority of artificial intelligence over dermatologists to diagnose melanoma from images. However, 30-50% of all melanomas and more than half of those in young patients evolve from initially benign lesions. Despite its...

Convolutional Neural Network Approach to Classify Skin Lesions Using Reflectance Confocal Microscopy.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
We propose an approach based on a convolutional neural network to classify skin lesions using the reflectance confocal microscopy (RCM) mosaics. Skin cancers are the most common type of cancers and a correct, early diagnosis significantly lowers both...

Deep Tissue Sequencing Using Hypodermoscopy and Augmented Intelligence to Analyze Atypical Pigmented Lesions.

Journal of cutaneous medicine and surgery
BACKGROUND: Over the past decade, new technologies, devices, and methods have been developed to assist in the diagnosis of cutaneous melanocytic lesions.