Abstract
Retinal diseases remain one of the leading causes of visual impairments in the world. The development of automated diagnostic methods can improve the efficiency and availability of the macular pathology mass screening programs. The objective of this work was to develop and validate deep learning algorithms detecting macular pathology (age-related macular degeneration, AMD) based on the analysis of color fundus photographs with and without data labeling. We used 1200 color fundus photographs from local databases, including 575 retinal images of AMD patients and 625 pictures of the retina of healthy people. The deep learning algorithm was deployed in the Faster RCNN neural network with ResNet50 for convolution. The process employed the transfer learning method. As a result, in the absence of labeling, the accuracy of the model was unsatisfactory (79%) because the neural network selected the areas of attention incorrectly. Data labeling improved the efficacy of the developed method: with the test dataset, the model determined the areas with informative features adequately, and the classification accuracy reached 96.6%. Thus, image data labeling significantly improves the accuracy of retinal color images recognition by a neural network and enables development and training of effective models with limited datasets.
Highlights
IntroductionПрименение разметки изображений значительно повышает точность распознавания цветных изображений сетчатки с помощью нейросетевых технологий и позволяет создавать эффективные модели при использовании ограниченных по объему наборов данных
Artificial intelligence capable of screening for eye diseases can mitigate the primary health care personnel shortage and reduce the clinical examination costs while increasing the number of patients reasonably referred to an ophthalmologist because of the suspected ophthalmic pathology [2]
This study showed that Faster RCNN neural network with ResNet50 enabling convolution can effectively differentiate between Age-related macular degeneration (AMD) patient fundus pictures and those of healthy retina
Summary
Применение разметки изображений значительно повышает точность распознавания цветных изображений сетчатки с помощью нейросетевых технологий и позволяет создавать эффективные модели при использовании ограниченных по объему наборов данных. Н. Светозарский — участие в сборе данных, анализ результатов, работа с литературой, написание текста рукописи; А. И. Бурсов — работа с литературой, разработка алгоритмов, редактирование рукописи; К. An effective retinal pathology early detection system that would be part of the mass preventive examination campaigns is yet to be deployed. Such systems require special logistics and dedicated staff, which, in addition to the one-time deployment expenses, translates into the need for regular funding to support the system and pay the people powering it. The disease manifests in soft drusen measuring 63 μm or above in the macular zone, hyperpigmentation and/or hypopigmentation of the pigment epithelium, detachment of pigment and neuroepithelium, pigment epithelium geographic atrophy, retinal hemorrhages and cicatricial changes in the retina [3]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
