Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks

Mohammed A. Al‐masni; Mugahed A. Al–antari; Mun‐Taek Choi; Seung‐Moo Han; Tae‐Seong Kim

doi:10.1016/j.cmpb.2018.05.027

ScienceGate Book Chapters

JOURNAL ARTICLE

Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks

Mohammed A. Al‐masni Mugahed A. Al–antari Mun‐Taek Choi Seung‐Moo Han Tae‐Seong Kim

Year: 2018 Journal: Computer Methods and Programs in Biomedicine Vol: 162 Pages: 221-231 Publisher: Elsevier BV

DOI: 10.1016/j.cmpb.2018.05.027

Get Full-Text PDF Get Analytical Report

Keywords:

Jaccard index Segmentation Computer science Artificial intelligence Pattern recognition (psychology) Deep learning Artifact (error) Skin lesion Pixel Image segmentation Convolutional neural network Computer vision Medicine Pathology

Metrics

460

Cited By

20.06

FWCI (Field Weighted Citation Impact)

110

Refs

0.99

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Cutaneous Melanoma Detection and Management

Health Sciences → Medicine → Oncology

AI in cancer detection

Physical Sciences → Computer Science → Artificial Intelligence

Nonmelanoma Skin Cancer Studies

Health Sciences → Medicine → Epidemiology

Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks

Metrics

Citation History

Topics

Related Documents

Lossless Full-Resolution Deep Learning Convolutional Networks for Skin Lesion Boundary Segmentation

Skin Lesion Segmentation Using Deep Convolutional Networks

A Convolutional Neural Network Based Skin Lesion Segmentation from Dermoscopy Images

Ensembles of Convolutional Neural Networks for Skin Lesion Dermoscopy Images Classification

Multi-scale Fully Convolutional DenseNets for Automated Skin Lesion Segmentation in Dermoscopy Images