Support vector machine and deep-learning object detection for localisation of hard exudates

Abstract Hard exudates are one of the main clinical findings in the retinal images of patients with diabetic retinopathy. Detecting them early significantly impacts the treatment of underlying diseases; therefore, there is a need for automated systems with high reliability. We propose a novel method...

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Autores principales:	Veronika Kurilová, Jozef Goga, Miloš Oravec, Jarmila Pavlovičová, Slavomír Kajan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7f38062e984643ea8b7d5b74483d7c12
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spelling	oai:doaj.org-article:7f38062e984643ea8b7d5b74483d7c122021-12-02T18:49:30ZSupport vector machine and deep-learning object detection for localisation of hard exudates10.1038/s41598-021-95519-02045-2322https://doaj.org/article/7f38062e984643ea8b7d5b74483d7c122021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95519-0https://doaj.org/toc/2045-2322Abstract Hard exudates are one of the main clinical findings in the retinal images of patients with diabetic retinopathy. Detecting them early significantly impacts the treatment of underlying diseases; therefore, there is a need for automated systems with high reliability. We propose a novel method for identifying and localising hard exudates in retinal images. To achieve fast image pre-scanning, a support vector machine (SVM) classifier was combined with a faster region-based convolutional neural network (faster R-CNN) object detector for the localisation of exudates. Rapid pre-scanning filtered out exudate-free samples using a feature vector extracted from the pre-trained ResNet-50 network. Subsequently, the remaining samples were processed using a faster R-CNN detector for detailed analysis. When evaluating all the exudates as individual objects, the SVM classifier reduced the false positive rate by 29.7% and marginally increased the false negative rate by 16.2%. When evaluating all the images, we recorded a 50% reduction in the false positive rate, without any decrease in the number of false negatives. The interim results suggested that pre-scanning the samples using the SVM prior to implementing the deep-network object detector could simultaneously improve and speed up the current hard exudates detection method, especially when there is paucity of training data.Veronika KurilováJozef GogaMiloš OravecJarmila PavlovičováSlavomír KajanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Veronika Kurilová Jozef Goga Miloš Oravec Jarmila Pavlovičová Slavomír Kajan Support vector machine and deep-learning object detection for localisation of hard exudates
description	Abstract Hard exudates are one of the main clinical findings in the retinal images of patients with diabetic retinopathy. Detecting them early significantly impacts the treatment of underlying diseases; therefore, there is a need for automated systems with high reliability. We propose a novel method for identifying and localising hard exudates in retinal images. To achieve fast image pre-scanning, a support vector machine (SVM) classifier was combined with a faster region-based convolutional neural network (faster R-CNN) object detector for the localisation of exudates. Rapid pre-scanning filtered out exudate-free samples using a feature vector extracted from the pre-trained ResNet-50 network. Subsequently, the remaining samples were processed using a faster R-CNN detector for detailed analysis. When evaluating all the exudates as individual objects, the SVM classifier reduced the false positive rate by 29.7% and marginally increased the false negative rate by 16.2%. When evaluating all the images, we recorded a 50% reduction in the false positive rate, without any decrease in the number of false negatives. The interim results suggested that pre-scanning the samples using the SVM prior to implementing the deep-network object detector could simultaneously improve and speed up the current hard exudates detection method, especially when there is paucity of training data.
format	article
author	Veronika Kurilová Jozef Goga Miloš Oravec Jarmila Pavlovičová Slavomír Kajan
author_facet	Veronika Kurilová Jozef Goga Miloš Oravec Jarmila Pavlovičová Slavomír Kajan
author_sort	Veronika Kurilová
title	Support vector machine and deep-learning object detection for localisation of hard exudates
title_short	Support vector machine and deep-learning object detection for localisation of hard exudates
title_full	Support vector machine and deep-learning object detection for localisation of hard exudates
title_fullStr	Support vector machine and deep-learning object detection for localisation of hard exudates
title_full_unstemmed	Support vector machine and deep-learning object detection for localisation of hard exudates
title_sort	support vector machine and deep-learning object detection for localisation of hard exudates
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7f38062e984643ea8b7d5b74483d7c12
work_keys_str_mv	AT veronikakurilova supportvectormachineanddeeplearningobjectdetectionforlocalisationofhardexudates AT jozefgoga supportvectormachineanddeeplearningobjectdetectionforlocalisationofhardexudates AT milosoravec supportvectormachineanddeeplearningobjectdetectionforlocalisationofhardexudates AT jarmilapavlovicova supportvectormachineanddeeplearningobjectdetectionforlocalisationofhardexudates AT slavomirkajan supportvectormachineanddeeplearningobjectdetectionforlocalisationofhardexudates
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Support vector machine and deep-learning object detection for localisation of hard exudates

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