# A Deep Learning Ensemble Method to Visual Acuity Measurement Using Fundus Images
###### Abstract  
Visual acuity (VA) is a measure of the ability to distinguish shapes and details of objects at a given distance and is a measure of the spatial resolution of the visual system. Vision is one of the basic health indicators closely related to a person's quality of life. It is one of the first basic tests done when an eye disease develops. VA is usually measured by using a Snellen chart or E-chart from a specific distance. However, in some cases, such as the unconsciousness of patients or diseases, i.e., dementia, it can be impossible to measure the VA using such traditional chart-based methodologies. This paper provides a machine learning-based VA measurement methodology that determines VA only based on fundus images. In particular, the levels of VA, conventionally divided into 11 levels, are grouped into four classes and three machine learning algorithms, one SVM model and two CNN models, are combined into an ensemble method in order to predict the corresponding VA level from a fundus image. Based on a performance evaluation conducted using randomly selected 4000 fundus images, we confirm that our ensemble method can estimate with 82.4% of the average accuracy for four classes of VA levels, in which each class of Class 1 to Class 4 identifies the level of VA with 88.5%, 58.8%, 88%, and 94.3%, respectively. To the best of our knowledge, this is the first paper on VA measurements based on fundus images using deep machine learning. visual acuity; fundus images; machine learning; ophthalmology; deep learning; SVM 2022 2022 20211 11 March 20222 20211 March 20222 202211 March 20222 202211 March 202221 March(5) patient's documentation. The general procedure for VA measurement recommended by the US national library of medicine can be as follows [2]:  
Position the patient, sitting or standing, at a distance of 6 m from the chart;  
Test the eyes one at a time, at first without any spectacles (if worn);  
Ask the patient to cover one eye with a plain occluder, card or tissue;  
Ask the patient to read from the top of the chart and from left to right;  
Record the VA for each eye in the patient's notes, stating whether it is with or without correction (spectacles). For example, Right VA = 0.1 with correction, Left VA = 0.2 with correction. ... In this procedure, the communication between VA examiner and examinee is essential to measure VA. However, it is not suitable or impossible to use the classical ways of measuring VA in the following occasions:  
When the patient is unable to use the measurement tool due to mobility difficulties;  
When the patient is unconscious state or lack of cooperation during the evaluation;  
When malingering should be strongly suspected;  
When an infant or a very young patient requires the measurement of VA. In particular, continuous visual acuity measurement is necessary to secure the quality of life after regaining consciousness for patients who remain unconscious for a long time. However, since the existing method of measuring vision requires the patient to have a conversation with a tester, it is impossible to measure the vision of a prolonged unconscious. In addition, in a situation, such as the recent COVID-19 pandemic, as social isolation is prolonged, patients feel that it is harder to visit hospitals than before. For this reason, it becomes challenging to manage people's visual sight via the traditional method of measuring eyesight. This paper provides a vision measurement method using deep learning-based ensemble methodology using fundus images. In this paper, we would overcome the following two problems:  
How can we measure the VA from an examinee who cannot communicate with the VA examiner or tries to present an incorrect VA value? How can we achieve a more accurate classifier when a dataset is fairly biased to certain classes in terms of the number of sample data? Fundus photography involves photographing the rear of an eye, which is also known as the fundus.