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Detection of glaucoma using three-stage training with EfficientNet

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Detection of glaucoma using three-stage training with EfficientNet

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dc.contributor.author de Zarzà, I. es_ES
dc.contributor.author de Curtò, J. es_ES
dc.contributor.author Tavares De Araujo Cesariny Calafate, Carlos Miguel es_ES
dc.date.accessioned 2023-10-19T18:01:54Z
dc.date.available 2023-10-19T18:01:54Z
dc.date.issued 2022-11 es_ES
dc.identifier.uri http://hdl.handle.net/10251/198418
dc.description.abstract [EN] This paper sets forth a methodology that is based on three-stage-training of a state-of-the-art network architecture previously trained on Imagenet, and iteratively finetuned in three steps; freezing first all layers, then re-training a specific number of them and finally training all the architecture from scratch, to achieve a system with high accuracy and reliability. To determine the performance of our technique a dataset consisting of 17.070 color cropped samples of fundus images, and that includes two classes, normal and abnormal, is used. Extensive evaluations using baselines models (VGG16, InceptionV3 and Resnet50) are carried out, in addition to thorough experimentation with the proposed pipeline using variants of EfficientNet and EfficientNetV2. The training procedure is described accurately, putting emphasis on the number of parameters trained, the confusion matrices (with analysis of false positives and false negatives), accuracy, and F1-score obtained at each stage of the proposed methodology. The results achieved show that the intelligent system presented for the task at hand is reliable, presents high precision, its predictions are consistent and the number of parameters needed to train are low compared to other alternatives. es_ES
dc.description.sponsorship This work is supported by the HK Innovation and Technology Commission (InnoHK Project CIMDA), the HK Research Grants Council (Project CityU 11204821) and City University of Hong Kong (Project 9610034). We acknowledge the support of Universitat Politècnica de València; R&D project PID2021-122580NB-I00, funded by MCIN/AEI/ 10.13039/501100011033 and ERDF. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Intelligent Systems with Applications es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Glaucoma es_ES
dc.subject Fundus images es_ES
dc.subject EfficientNet es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title Detection of glaucoma using three-stage training with EfficientNet es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.iswa.2022.200140 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI//PID2021-122580NB-I00//SISTEMAS INTELIGENTES DE SENSORIZACIÓN PARA ECOSISTEMAS, ESPACIOS URBANOS Y MOVILIDAD SOSTENIBLE/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/RGC//11204821//Project CityU/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.description.bibliographicCitation De Zarzà, I.; De Curtò, J.; Tavares De Araujo Cesariny Calafate, CM. (2022). Detection of glaucoma using three-stage training with EfficientNet. Intelligent Systems with Applications. 16:1-10. https://doi.org/10.1016/j.iswa.2022.200140 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.iswa.2022.200140 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 10 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 16 es_ES
dc.identifier.eissn 2667-3053 es_ES
dc.relation.pasarela S\474211 es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.contributor.funder Research Grant Council, Hong Kong es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.contributor.funder Innovation and Technology Commission - Hong Kong es_ES
upv.costeAPC 655 es_ES


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