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Disease Classification within Dermascopic Images Using features extracted by ResNet50 and classification through Deep Forest
In this report we propose a classification technique for skin lesion ima...
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Skin Lesion Classification Using Deep Multi-scale Convolutional Neural Networks
We present a deep learning approach to the ISIC 2017 Skin Lesion Classif...
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Segmentation and Classification of Skin Lesions for Disease Diagnosis
In this paper, a novel approach for automatic segmentation and classific...
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Automatic Skin Lesion Analysis using Large-scale Dermoscopy Images and Deep Residual Networks
Malignant melanoma has one of the most rapidly increasing incidences in ...
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High Accuracy Classification of White Blood Cells using TSLDA Classifier and Covariance Features
Creating automated processes in different areas of medical science with ...
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The Cyborg Astrobiologist: Scouting Red Beds for Uncommon Features with Geological Significance
The `Cyborg Astrobiologist' (CA) has undergone a second geological field...
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The Combined Technique for Detection of Artifacts in Clinical Electroencephalograms of Sleeping Newborns
In this paper we describe a new method combining the polynomial neural n...
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Automatic Detection of Blue-White Veil and Related Structures in Dermoscopy Images
Dermoscopy is a non-invasive skin imaging technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One of the most important features for the diagnosis of melanoma in dermoscopy images is the blue-white veil (irregular, structureless areas of confluent blue pigmentation with an overlying white "ground-glass" film). In this article, we present a machine learning approach to the detection of blue-white veil and related structures in dermoscopy images. The method involves contextual pixel classification using a decision tree classifier. The percentage of blue-white areas detected in a lesion combined with a simple shape descriptor yielded a sensitivity of 69.35 and a specificity of 89.97 rises to 78.20 feature for melanoma recognition.
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