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|Title:||Machine learning applied to retinal image processing for glaucoma detection: review and perspective|
|Authors:||Barros, Daniele M. S.|
Moura, Julio C. C.
Freire, Cefas R.
Taleb, Alexandre C.
Valentim, Ricardo Alexsandro de Medeiros
Morais, Philippi Sedir Grilo de
|Keywords:||Machine learning;Deep learning;Retinal image processing;Glaucoma;Classifcation|
|Publisher:||Biomedical Engineering Online|
|Citation:||BARROS, DANIELE M. S.; MOURA, JULIO C. C.; FREIRE, CEFAS R.; TALEB, ALEXANDRE C.; VALENTIM, RICARDO A. M.; MORAIS, PHILIPPI S. G.. Machine learning applied to retinal image processing for glaucoma detection: review and perspective. Biomedical Engineering Online, v. 19, p. 20, 2020. Disponível em: https://biomedical-engineering-online.biomedcentral.com/articles/10.1186/s12938-020-00767-2. Acesso em: 10 Junho 2020. https://doi.org/10.1186/s12938-020-00767-2.|
|Portuguese Abstract:||Introduction: This is a systematic review on the main algorithms using machine learning (ML) in retinal image processing for glaucoma diagnosis and detection. ML has proven to be a significant tool for the development of computer aided technology. Furthermore, secondary research has been widely conducted over the years for ophthalmologists. Such aspects indicate the importance of ML in the context of retinal image processing. Methods: The publications that were chosen to compose this review were gathered from Scopus, PubMed, IEEEXplore and Science Direct databases. Then, the papers published between 2014 and 2019 were selected. Researches that used the segmented optic disc method were excluded. Moreover, only the methods which applied the classification process were considered. The systematic analysis was performed in such studies and, thereupon, the results were summarized. Discussion: Based on architectures used for ML in retinal image processing, some studies applied feature extraction and dimensionality reduction to detect and isolate important parts of the analyzed image. Differently, other works utilized a deep convolutional network. Based on the evaluated researches, the main difference between the architectures is the number of images demanded for processing and the high computational cost required to use deep learning techniques. Conclusions: All the analyzed publications indicated it was possible to develop an automated system for glaucoma diagnosis. The disease severity and its high occurrence rates justify the researches which have been carried out. Recent computational techniques, such as deep learning, have shown to be promising technologies in fundus imaging. Although such a technique requires an extensive database and high computational costs, the studies show that the data augmentation and transfer learning techniques have been applied as an alternative way to optimize and reduce networks training|
|Appears in Collections:||CT - DEB - Artigos publicados em periódicos|
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