AIMC Topic: Dermoscopy

Clear Filters Showing 121 to 130 of 181 articles

Skin cancer detection by deep learning and sound analysis algorithms: A prospective clinical study of an elementary dermoscope.

EBioMedicine May 14, 2019
BACKGROUND: Skin cancer (SC), especially melanoma, is a growing public health burden. Experimental studies have indicated a potential diagnostic role for deep learning (DL) algorithms in identifying SC at varying sensitivities. Previously, it was dem...
ROC Curve Image Processing, Computer-Assisted Medical Informatics Aged Middle Aged Aged, 80 and over Deep Learning Young Adult Adolescent Adult Sensitivity and Specificity Male Female Skin Neoplasms Dermoscopy Humans Algorithms
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Skin Lesion Classification Using CNNs With Patch-Based Attention and Diagnosis-Guided Loss Weighting.

IEEE transactions on bio-medical engineering May 9, 2019
OBJECTIVE: This paper addresses two key problems of skin lesion classification. The first problem is the effective use of high-resolution images with pretrained standard architectures for image classification. The second problem is the high-class imb...
Dermoscopy Deep Learning Skin Image Interpretation, Computer-Assisted Humans Databases, Factual Skin Neoplasms
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A comparative study of deep learning architectures on melanoma detection.

Tissue & cell Apr 22, 2019
Melanoma is the most aggressive type of skin cancer, which significantly reduces the life expectancy. Early detection of melanoma can reduce the morbidity and mortality associated with skin cancer. Dermoscopic images acquired by dermoscopic instrumen...
Skin Dermoscopy Deep Learning Image Processing, Computer-Assisted Humans Melanoma Skin Neoplasms
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Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task.

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 ...
Humans Melanoma Deep Learning Dermatologists Hospitals, University Germany Skin Neoplasms Dermoscopy Sensitivity and Specificity Nevus
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A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task.

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 ...
Dermatologists Dermatology Image Interpretation, Computer-Assisted Skin Neoplasms Dermoscopy Sensitivity and Specificity Humans Neural Networks, Computer Melanoma
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Attention Residual Learning for Skin Lesion Classification.

IEEE transactions on medical imaging Jan 21, 2019
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...
Neural Networks, Computer Databases, Factual Dermoscopy Skin Diseases Image Interpretation, Computer-Assisted Humans Signal Processing, Computer-Assisted Skin
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Dermoscopy diagnosis of cancerous lesions utilizing dual deep learning algorithms via visual and audio (sonification) outputs: Laboratory and prospective observational studies.

EBioMedicine Jan 20, 2019
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 ...
Aged, 80 and over Young Adult Adult Humans Male Skin Neoplasms Artificial Intelligence Middle Aged Dermoscopy Sound Deep Learning Aged Telemedicine Adolescent Algorithms ROC Curve Retrospective Studies Skin Female
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Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering.

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...
Humans Neural Networks, Computer Melanoma Fuzzy Logic Cluster Analysis Skin Neoplasms Dermoscopy Sensitivity and Specificity
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Artificial Intelligence Based Skin Classification Using GMM.

Journal of medical systems Nov 20, 2018
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...
Humans Skin Diseases Dermoscopy Neural Networks, Computer Pattern Recognition, Automated Image Processing, Computer-Assisted Wavelet Analysis Artificial Intelligence Skin
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Domain-specific classification-pretrained fully convolutional network encoders for skin lesion segmentation.

Computers in biology and medicine Nov 16, 2018
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...
Image Processing, Computer-Assisted Humans Skin Neural Networks, Computer Skin Diseases Dermoscopy
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