TY - GEN
T1 - Exudates Detection Based on SSD MobileNet for Referable Diabetic Retinopathy
AU - García-Nonoal, Zaira
AU - Nakano-Miyatake, Mariko
AU - Perez-Meana, Héctor
AU - Gonzalez-H.leon, Ana
N1 - Publisher Copyright:
© 2022 The authors and IOS Press. All rights reserved.
PY - 2022/9/14
Y1 - 2022/9/14
N2 - Automatic detection of the referable Diabetic Retinopathy (RDR) has become essential in diabetic patients, especially who live in the remote regions, to avoid a serious visual impairment. For this reason, different approaches have been developed with the aim of detect and segment the principal DR lesions for automatic diagnosis of the RDR. Exudate is one of the DR lesions and if these lesions appear in the macular region, a diabetic macular edema (DME) can be suspected and a detailed analysis by ophthalmologist is required. Then it is important to detect these lesions with their position related to the macular region to determine the danger level. This paper presents an automatic method to localize the exudates and optic disc (OD) using Single Shot Detector (SSD) scheme based on MobileNet-V1 as base network to determine if the risk of DME exits to indicate patients the necessity of consultation by ophthalmologist. The proposed system is evaluated using MESSIDOR Database, providing 89.15% accuracy, 88.17% sensitivity and 91.67% specificity.
AB - Automatic detection of the referable Diabetic Retinopathy (RDR) has become essential in diabetic patients, especially who live in the remote regions, to avoid a serious visual impairment. For this reason, different approaches have been developed with the aim of detect and segment the principal DR lesions for automatic diagnosis of the RDR. Exudate is one of the DR lesions and if these lesions appear in the macular region, a diabetic macular edema (DME) can be suspected and a detailed analysis by ophthalmologist is required. Then it is important to detect these lesions with their position related to the macular region to determine the danger level. This paper presents an automatic method to localize the exudates and optic disc (OD) using Single Shot Detector (SSD) scheme based on MobileNet-V1 as base network to determine if the risk of DME exits to indicate patients the necessity of consultation by ophthalmologist. The proposed system is evaluated using MESSIDOR Database, providing 89.15% accuracy, 88.17% sensitivity and 91.67% specificity.
KW - Deep Learning
KW - Diabetic Macular Edema
KW - Diabetic Retinopathy
KW - Exudates
KW - MobileNet
KW - Object Detection
KW - Single Shot Detector
UR - http://www.scopus.com/inward/record.url?scp=85139744146&partnerID=8YFLogxK
U2 - 10.3233/FAIA220257
DO - 10.3233/FAIA220257
M3 - Contribución a la conferencia
AN - SCOPUS:85139744146
T3 - Frontiers in Artificial Intelligence and Applications
SP - 261
EP - 271
BT - New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022
A2 - Fujita, Hamido
A2 - Watanobe, Yutaka
A2 - Azumi, Takuya
PB - IOS Press BV
T2 - 21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022
Y2 - 20 September 2022 through 22 September 2022
ER -
