Academic Journal
Support vector machine and deep-learning object detection for localisation of hard exudates
| العنوان: | Support vector machine and deep-learning object detection for localisation of hard exudates |
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| المؤلفون: | Veronika Kurilová, Jozef Goga, Miloš Oravec, Jarmila Pavlovičová, Slavomír Kajan |
| المصدر: | Scientific Reports, Vol 11, Iss 1, Pp 1-9 (2021) |
| بيانات النشر: | Nature Portfolio, 2021. |
| سنة النشر: | 2021 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Medicine, Science |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2045-2322 |
| Relation: | https://doaj.org/toc/2045-2322 |
| DOI: | 10.1038/s41598-021-95519-0 |
| URL الوصول: | https://doaj.org/article/7f38062e984643ea8b7d5b74483d7c12 |
| رقم الانضمام: | edsdoj.7f38062e984643ea8b7d5b74483d7c12 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20452322 |
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| DOI: | 10.1038/s41598-021-95519-0 |