AIMC Topic: Dermoscopy

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Detecting anomalous growth of skin lesion using threshold-based segmentation algorithm and Fuzzy K-Nearest Neighbor classifier.

Journal of cancer research and therapeutics Jan 1, 2020
CONTEXT: Skin cancer is a complex and life-threatening disease caused primarily by genetic instability and accumulation of multiple molecular alternations.
Humans Algorithms Image Processing, Computer-Assisted Support Vector Machine Fuzzy Logic Cluster Analysis Melanoma Skin Neoplasms Dermoscopy
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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 Jul 1, 2019
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...
Skin Neoplasms Humans Carcinoma, Basal Cell Nevus, Pigmented Sensitivity and Specificity Neural Networks, Computer Dermoscopy Microscopy, Confocal Melanoma
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Light Field Image Dataset of Skin Lesions.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2019
Light field imaging technology has been attracting increasing interest because it enables capturing enriched visual information and expands the processing capabilities of traditional 2D imaging systems. Dense multiview, accurate depth maps and multip...
Skin Diseases Machine Learning Algorithms Dermoscopy Humans
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Computational neural network in melanocytic lesions diagnosis: artificial intelligence to improve diagnosis in dermatology?

European journal of dermatology : EJD Apr 1, 2019
Diagnosis in dermatology is largely based on contextual factors going far beyond the visual and dermoscopic inspection of a lesion. Diagnostic tools such as the different types of dermoscopy, confocal microscopy and optical coherence tomography (OCT)...
Artificial Intelligence Algorithms Melanoma Humans Neural Networks, Computer Deep Learning Image Processing, Computer-Assisted Microscopy, Confocal Tomography, Optical Coherence Skin Neoplasms Dermoscopy Sensitivity and Specificity Skin Dermatology Melanocytes
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Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks.

JAMA dermatology Jan 1, 2019
IMPORTANCE: Convolutional neural networks (CNNs) achieve expert-level accuracy in the diagnosis of pigmented melanocytic lesions. However, the most common types of skin cancer are nonpigmented and nonmelanocytic, and are more difficult to diagnose.
Reproducibility of Results Skin Adult Male Humans Follow-Up Studies Diagnosis, Differential Middle Aged ROC Curve Female Skin Neoplasms Dermoscopy Neural Networks, Computer Algorithms Retrospective Studies
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Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists.

Annals of oncology : official journal of the European Society for Medical Oncology Aug 1, 2018
BACKGROUND: Deep learning convolutional neural networks (CNN) may facilitate melanoma detection, but data comparing a CNN's diagnostic performance to larger groups of dermatologists are lacking.
Humans Clinical Competence ROC Curve Image Processing, Computer-Assisted International Cooperation Skin Dermatologists Dermoscopy Cross-Sectional Studies Deep Learning Retrospective Studies Melanoma Skin Neoplasms
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Dense deconvolution net: Multi path fusion and dense deconvolution for high resolution skin lesion segmentation.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2018
BACKGROUND: Dermoscopy imaging has been a routine examination approach for skin lesion diagnosis. Accurate segmentation is the first step for automatic dermoscopy image assessment.
Neural Networks, Computer Skin Neoplasms Dermoscopy Nevus Image Processing, Computer-Assisted Diagnosis, Differential Humans
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Deep Tissue Sequencing Using Augmented Intelligence to Probe Melanocytic Lesions.

Journal of cutaneous medicine and surgery Jan 1, 2017
Dermoscopy Diagnosis, Computer-Assisted Artificial Intelligence Melanoma Skin Neoplasms Humans
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A deep bag-of-features model for the classification of melanomas in dermoscopy images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Aug 1, 2016
Deep learning and unsupervised feature learning have received great attention in past years for their ability to transform input data into high level representations using machine learning techniques. Such interest has been growing steadily in the fi...
Dermoscopy Databases, Factual ROC Curve Melanoma Humans Skin Neoplasms Image Processing, Computer-Assisted Neural Networks, Computer Machine Learning
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Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging.

Journal of biomedical optics Dec 1, 2015
Multispectral imaging (MSI) was implemented to develop a burn tissue classification device to assist burn surgeons in planning and performing debridement surgery. To build a classification model via machine learning, training data accurately represen...
Skin Dermoscopy Burns Image Enhancement Reproducibility of Results Image Interpretation, Computer-Assisted Machine Learning Sensitivity and Specificity Optical Imaging Artifacts Subtraction Technique Algorithms Animals Spectrum Analysis Swine Pattern Recognition, Automated
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