# A Deep Learning Ensemble Method to Visual Acuity Measurement Using Fundus Images
###### Abstract
It is a photo image most popularly used in examining more than 38 types of eye diseases, such as age-related macular degeneration, neoplasm of choroid, choriori-retinal inflammation or scars, glaucoma, retinal detachment and defects, and so on. Fundus imaging has been advanced to decrease preventable visual morbidity by allowing easy and timely fundus screening. In particular, the usability and portability of fundus screening have been continuously advanced for the last two decades. Furthermore, recently, there have been significant technological advances that have radicalized retinal photography. Improvements in telecommunications and smartphones are two remarkable breakthroughs that have made ophthalmic screening in remote areas a realizable possibility [3]. We address the above first problem with the high availability of fundus images. We would estimate the VA by capturing a fundus image from a VA examinee and using a VA classifier based on a deep machine learning technique. In this paper, 11 levels from 0.0 to 1.0 (step by 0.1) of VA levels are grouped into four classes according to ophthalmologist doctor's needs. To tackle the second problem, we adopt an ensemble approach consisting of three machine learning models. In the medical field, it is very difficult to obtain a balanced size of a medical dataset because the dataset of normal cases is much larger than those of abnormal cases. The dataset of VA measurement results has the same issue; the cases of a lower VA level, in reality, are much less than those of a higher VA level. For this reason, it is difficult to adopt a classical CNN model for such unbalanced datasets of VA levels in a classical way. In our ensemble approach, three machine learning models and techniques are combined to the classification of VA level groups or VA levels using their best classification performance. The contributions of this paper are to  
* to present a deep-learning-based VA measurement approach using fundus images;
* to demonstrate the feasibility and effectiveness of an ensemble approach to overcome the difficulties of obtaining datasets with a balanced size, and
* to present a VA measurement alternative for the examinee who is not easy or has no possible way to communicate with the VA examiner. To the best of our knowledge, this is the first paper on the VA measurement based on fundus images using machine learning. This paper is organized as follows: In Section 2, we present related work relevant to this work. In Section 3, we present a simple description on fundus images and the datasets consisting of fundus image and VA measurement data that we obtain from hospitals. Section 4 presents the main idea of our approach, a deep learning-based ensemble methodology for VA Measurement. We discuss the reason why an ensemble method is appropriate for our work and individual machine learning techniques comprising the ensemble method. In Section 5, we present the validation results of our proposed 4-Class VA classifier based on fundus images with various metrics of machine learning performance evaluation and a comparison of our ensemble method against the VGG-19-based CNN model. Finally, we conclude this paper in Section 6.