European journal of cancer (Oxford, England : 1990)
Apr 10, 2019
BACKGROUND: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For ...
European journal of cancer (Oxford, England : 1990)
Mar 8, 2019
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 ...
Automated skin lesion classification in dermoscopy images is an essential way to improve the diagnostic performance and reduce melanoma deaths. Although deep convolutional neural networks (DCNNs) have made dramatic breakthroughs in many image classif...
BACKGROUND: Early diagnosis of skin cancer lesions by dermoscopy, the gold standard in dermatological imaging, calls for a diagnostic upscale. The aim of the study was to improve the accuracy of dermoscopic skin cancer diagnosis through use of novel ...
International journal of medical informatics
Jan 18, 2019
OBJECTIVE: Melanoma is a dangerous form of the skin cancer responsible for thousands of deaths every year. Early detection of melanoma is possible through visual inspection of pigmented lesions over the skin, treated with simple excision of the cance...
This study describes the usage of neural community based on the texture evaluation of pores and skin a variety of similarities in their signs, inclusive of Measles (rubella), German measles (rubella), and Chickenpox etc. In fashionable, these illness...
BACKGROUND AND OBJECTIVE: Fully convolutional neural networks have been shown to perform well for automated skin lesion segmentation on digital dermatoscopic images. Our concept is that transferring encoder weights from a network trained on a classif...
While convolutional neural networks (CNNs) have successfully been applied for skin lesion classification, previous studies have generally considered only a single clinical/macroscopic image and output a binary decision. In this work, we have presente...
BACKGROUND: Application of deep-learning technology to skin cancer classification can potentially improve the sensitivity and specificity of skin cancer screening, but the number of training images required for such a system is thought to be extremel...
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