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

• Published in: Scientific reports Vol. 11; no. 1; p. 16045
• Main Authors: Kurilová, Veronika, Goga, Jozef, Oravec, Miloš, Pavlovičová, Jarmila, Kajan, Slavomír
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 06.08.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/114/1305; 631/114/1564; 692/699/3161; Deep learning; Diabetes mellitus; Diabetic retinopathy; Exudates; Humanities and Social Sciences; Learning algorithms; Localization; multidisciplinary; Neural networks; Retina; Retinopathy; Scanning; Science; Science (multidisciplinary); Sensors; Support vector machines
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-95519-0

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.