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: | Jarmila Pavlovicova, Jozef Goga, Slavomir Kajan, Milos Oravec, Veronika Kurilova |
| المصدر: | Scientific Reports, Vol 11, Iss 1, Pp 1-9 (2021) Scientific Reports |
| بيانات النشر: | Nature Portfolio, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Multidisciplinary, Computer science, business.industry, Deep learning, Feature vector, Science, Detector, Pattern recognition, Convolutional neural network, Article, Object detection, Computational biology and bioinformatics, Support vector machine, Image processing, Machine learning, Classifier (linguistics), Medicine, False positive rate, Artificial intelligence, business, Eye diseases |
| الوصف: | 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. |
| اللغة: | English |
| تدمد: | 2045-2322 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5440106ee53ed9744134231761c6f0d https://doaj.org/article/7f38062e984643ea8b7d5b74483d7c12 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....e5440106ee53ed9744134231761c6f0d |
| قاعدة البيانات: | OpenAIRE |
Find this article in full text from Springer Verlag. | تدمد: | 20452322 |
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