Exudates Detection Based on SSD MobileNet for Referable Diabetic Retinopathy

Zaira García-Nonoal, Mariko Nakano-Miyatake, Héctor Perez-Meana, Ana Gonzalez-H.leon

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaNew 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
EditoresHamido Fujita, Yutaka Watanobe, Takuya Azumi
EditorialIOS Press BV
Páginas261-271
Número de páginas11
ISBN (versión digital)9781643683164
DOI
EstadoPublicada - 14 sep. 2022
Evento21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022 - Kitakyushu, Japón
Duración: 20 sep. 202222 sep. 2022

Serie de la publicación

NombreFrontiers in Artificial Intelligence and Applications
Volumen355
ISSN (versión impresa)0922-6389
ISSN (versión digital)1879-8314

Conferencia

Conferencia21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022
País/TerritorioJapón
CiudadKitakyushu
Período20/09/2222/09/22

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