AIMC Topic: Skin Diseases

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Artificial Intelligence in Dermatology: A Primer.

The Journal of investigative dermatology Mar 27, 2020
Artificial intelligence is becoming increasingly important in dermatology, with studies reporting accuracy matching or exceeding dermatologists for the diagnosis of skin lesions from clinical and dermoscopic images. However, real-world clinical valid...
Referral and Consultation Triage Skin Telemedicine Deep Learning Dermatology Image Processing, Computer-Assisted Skin Diseases Humans
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Deep Learning-Based Methods for Automatic Diagnosis of Skin Lesions.

Sensors (Basel, Switzerland) Mar 21, 2020
The main purpose of the study was to develop a high accuracy system able to diagnose skin lesions using deep learning-based methods. We propose a new decision system based on multiple classifiers like neural networks and feature-based methods. Each c...
Humans Neural Networks, Computer Algorithms Deep Learning Support Vector Machine Artificial Intelligence Machine Learning Skin Diseases
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Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network.

Sensors (Basel, Switzerland) Mar 13, 2020
Clinical treatment of skin lesion is primarily dependent on timely detection and delimitation of lesion boundaries for accurate cancerous region localization. Prevalence of skin cancer is on the higher side, especially that of melanoma, which is aggr...
Neural Networks, Computer Algorithms Skin Neoplasms Skin Skin Diseases Dermoscopy Humans Artifacts Image Processing, Computer-Assisted
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Towards Interpretable Skin Lesion Classification with Deep Learning Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
Skin disease is a prevalent condition all over the world. Computer vision-based technology for automatic skin lesion classification holds great promise as an effective screening tool for early diagnosis. In this paper, we propose an accurate and inte...
Models, Biological Skin Neoplasms Humans Keratosis Neural Networks, Computer Skin Diseases Deep Learning Nevus
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GP-CNN-DTEL: Global-Part CNN Model With Data-Transformed Ensemble Learning for Skin Lesion Classification.

IEEE journal of biomedical and health informatics Feb 28, 2020
Precise skin lesion classification is still challenging due to two problems, i.e., (1) inter-class similarity and intra-class variation of skin lesion images, and (2) the weak generalization ability of single Deep Convolutional Neural Network trained...
Skin Diseases Image Interpretation, Computer-Assisted Dermoscopy Deep Learning Humans Neural Networks, Computer
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A patient-oriented, general-practitioner-level, deep-learning-based cutaneous pigmented lesion risk classifier on a smartphone.

The British journal of dermatology Feb 3, 2020
Skin Skin Diseases Deep Learning Smartphone Humans
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Improved skin lesions detection using color space and artificial intelligence techniques.

The Journal of dermatological treatment Jan 3, 2020
Automatic skin lesion image identification is of utmost importance to develop a fully automatized computer-aided skin analysis system. This will be helping the medical practitioners to provide skin lesions disease treatment more efficiently and effe...
Humans Artificial Intelligence Image Processing, Computer-Assisted Skin Diseases Algorithms
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Deep Learning and Mathematical Models in Dermatology.

Actas dermo-sifiliograficas Jan 3, 2020
Humans Machine Learning Skin Diseases Deep Learning Dermatology Models, Theoretical
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Prediction of Skin Disease with Three Different Feature Selection Techniques Using Stacking Ensemble Method.

Applied biochemistry and biotechnology Dec 16, 2019
Skin disease is the most common problem between people. Due to pollution and deployment of ozone layer, harmful UV rays of sun burn the skin and develop various types of skin diseases. Nowadays, machine learning and deep learning algorithms are gener...
Diagnosis, Computer-Assisted Support Vector Machine Machine Learning Algorithms Bayes Theorem Humans Skin Diseases
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Utilizing Machine Learning for Image Quality Assessment for Reflectance Confocal Microscopy.

The Journal of investigative dermatology Dec 12, 2019
In vivo reflectance confocal microscopy (RCM) enables clinicians to examine lesions' morphological and cytological information in epidermal and dermal layers while reducing the need for biopsies. As RCM is being adopted more widely, the workflow is e...
Feasibility Studies Skin Diseases Quality Control Humans Diagnosis, Differential Skin Machine Learning Image Processing, Computer-Assisted Dermoscopy Microscopy, Confocal
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