# A Deep Learning Ensemble Method to Visual Acuity Measurement Using Fundus Images
## 6 Conclusions  
Visual sight is one of the most sensing capabilities of humans. Visual Acuity (VA) is a fundamental measure of the ability of the eye to distinguish shapes and the details of objects at a given distance. It is a primary indicator of eye health and the results of medical treatment for eye diseases. VA is typically measured using VA measuring tools, such as Snellen or E-Chart. In particular, communication with the tester is essential. However, it is not suitable or impossible to use the classical ways of measuring VA for patients under mobility difficulties, unconscious states, or lack of cooperation, and an infant or very young patient. To solve those problems, we present an ensemble method based on machine learning based on fundus images and VA information of patients. Fundus photography is one of the most popularly used photo images for an eye examination and rarely needs the cooperation of patients to obtain the image. In our approach, 11 classes in classical VA measurement are abstracted into four classes, Classes 1-4, to overcome the discrepancy problem of fundus image data quantity for each of 11 classes. In the ensemble method, the VA is measured in two steps: In the first step, Classes 1-2 and Classes 3-4 are classified as either Class A or Class B. In the second step, the fundus images in Class A is classified as either Class 1 or Class 2 and those in Class B is classified as either Class 3 or Class 4. We use three different machine learning techniques for each classification: VGG-19-base CNN, EfficientNet-B7-based CNN, and SVM-RBF-Kernel. We evaluated the three techniques for each classification of individual steps and selected one of them that shows the best classification performance for each step. From our validation of the 4-Class VA classifier using 4000 fundus images from each of the four classes, we obtained 88.5%, 58.8%, 88 %, and 94.3% of classification accuracy for each level of four classes, respectively, and the classification accuracy of 82.4% on average. To make our approach useful in practice, we have more challenges to overcome. For example, the density of the background pigmentation of the fundus oculi is dependent on race. We need to obtain more data from other countries and races to overcome this problem. In addition, the examinee's subjectivity in measuring vision acuity may degrade the collected data quality. In addition, the fundus image shows the functional status of the eye, thus measuring visual acuity with only fundus images have limitations since our vision depends on both the function of the eye and the function of the brain. Conceptualization, J.H.K., W.L. and Y.S.H. ; methodology, J.H.K. ; software, S.N., S.S., S.R. and E.J. ; validation, S.N., S.S., S.R. and E.J. ; investigation, S.N., S.S., S.R. and E.J. ; data curation, T.S.K. ; writing--original draft preparation, J.H.K., S.N., S.S., R. and E.J. ; writing--review and editing, S.L. ([PERSON]) and K.H.K. ; visualization, S.N., S.S., S.R. and E.J. ; supervision, W.L. and Y.S.H. ; project administration, J.H.K. and Y.S.H. ; funding acquisition, J.H.K., H.C. and S.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research was funded by Regional Innovation Strategy (RIS) through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (MOE)(2021 RIS-003) and National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2020R1A2C1014855).