An ensemble deep learning based approach for red lesion detection in fundus images

Computer Methods and Programs in Biomedicine

Volumn 153, Issue , 2018, Pages 115-127

  Orlando, José Ignacio ^a,b   Prokofyeva, Elena ^d,e   del Fresno, Mariana ^a,c   Blaschko, Matthew B ^f  

^a UNIVERSIDAD NACIONAL DEL CENTRO DE LA PROVINCIA DE BUENOS AIRES   (Argentina)

^b Universidad Nacional de Río Cuarto Consejo Nacional de Investigaciones Científicas y Técnicas   (Argentina)

^c KU Leuven ^*  (Argentina)

^d SCIENTIFIC INSTITUTE OF PUBLIC HEALTH   (Belgium)

^e Federal Agency for Medicines and Health Products Belgium   (Belgium)

^f UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

Deep learning; Diabetic retinopathy; Fundus images; Red lesion detection

Indexed keywords

COMPUTER AIDED DIAGNOSIS; DECISION TREES; EYE PROTECTION; NEURAL NETWORKS; OPEN SYSTEMS;

COMPUTER ASSISTED DIAGNOSIS; CONVOLUTIONAL NEURAL NETWORKS (CNN); DIABETIC RETINOPATHY; FALSE POSITIVE DETECTION; FUNDUS IMAGE; OPEN SOURCE IMPLEMENTATION; RANDOM FOREST CLASSIFIER; RED LESIONS;

DEEP LEARNING;

AREA UNDER THE CURVE; ARTICLE; BLEEDING; CONTROLLED STUDY; DIABETIC RETINOPATHY; EYE FUNDUS; MICROANEURYSM; RANDOM FOREST; RECEIVER OPERATING CHARACTERISTIC; RETINA IMAGE; SENSITIVITY AND SPECIFICITY; ARTIFICIAL NEURAL NETWORK; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC IMAGING; HUMAN; MACHINE LEARNING; PATHOLOGY;

DIABETIC RETINOPATHY; FUNDUS OCULI; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MACHINE LEARNING; MICROANEURYSM; NEURAL NETWORKS (COMPUTER);

EID: 85031774716     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2017.10.017     Document Type: Article

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