Normal retina pictures11/2/2023 Second, the current state-of-the-art of algorithms combining retinal image processing was determined. Initially, the study objective was defined. , as follows: framing questions for a review, identifying relevant work, assessing the quality of studies, summarizing the evidence, and interpreting the findings. The methods utilized in the present study were based on the five steps described by Khan et al. 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.Ī literature review aims to synthesize works on a research source to aid further investigations. Recent computational techniques, such as deep learning, have shown to be promising technologies in fundus imaging. The disease severity and its high occurrence rates justify the researches which have been carried out. ConclusionsĪll the analyzed publications indicated it was possible to develop an automated system for glaucoma diagnosis. 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. Differently, other works utilized a deep convolutional network. Discussionīased 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. The systematic analysis was performed in such studies and, thereupon, the results were summarized. Moreover, only the methods which applied the classification process were considered. Researches that used the segmented optic disc method were excluded. Then, the papers published between 20 were selected. The publications that were chosen to compose this review were gathered from Scopus, PubMed, IEEEXplore and Science Direct databases. Such aspects indicate the importance of ML in the context of retinal image processing. Furthermore, secondary research has been widely conducted over the years for ophthalmologists. ML has proven to be a significant tool for the development of computer aided technology. This is a systematic review on the main algorithms using machine learning (ML) in retinal image processing for glaucoma diagnosis and detection.
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