Research Article Deep Learning for Ocular Disease Recognition: An Inner-Class Balance Md Shakib Khan , 1 Nafisa Tafshir, 1 Kazi Nabiul Alam , 1 Abdur Rab Dhruba , 1 Mohammad Monirujjaman Khan , 1 Amani Abdulrahman Albraikan , 2 and Faris A. Almalki 3 1 Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka 1229, Bangladesh 2 Department of Computer Science, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia 3 Department of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia Correspondence should be addressed to Mohammad Monirujjaman Khan; monirkhan.qmul@gmail.com Received 24 February 2022; Revised 18 March 2022; Accepted 12 April 2022; Published 28 April 2022 Academic Editor: Muhammad Zubair Asghar Copyright © 2022 Md Shakib Khan et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. It can be challenging for doctors to identify eye disorders early enough using fundus pictures. Diagnosing ocular illnesses by hand is time-consuming, error-prone, and complicated. erefore, an automated ocular disease detection system with computer-aided tools is necessary to detect various eye disorders using fundus pictures. Such a system is now possible as a consequence of deep learning algorithms that have improved image classification capabilities. A deep-learning-based approach to targeted ocular detection is presented in this study. For this study, we used state-of-the-art image classification algorithms, such as VGG-19, to classify the ODIR dataset, which contains 5000 images of eight different classes of the fundus. ese classes represent different ocular diseases. However, the dataset within these classes is highly unbalanced. To resolve this issue, the work suggested converting this multiclass classification problem into a binary classification problem and taking the same number of images for both classifications. en, the binary classifications were trained with VGG-19. e accuracy of the VGG-19 model was 98.13% for the normal (N) versus pathological myopia (M) class; the model reached an accuracy of 94.03% for normal (N) versus cataract (C), and the model provided an accuracy of 90.94% for normal (N) versus glaucoma (G). All of the other models also improve the accuracy when the data is balanced. 1. Introduction e diagnosis of ocular pathology using fundus images is a significant difficulty in health care [1]. Ocular disease refers to any condition or disorder that interferes with the eye’s capacity to operate correctly or has a detrimental impact on the eye’s visual acuity [2]. Almost everyone suffers from vision prob- lems during their lifetime. Some are minors that do not appear on claims or are easily treated at home, while others need a specialist’s attention [3]. Globally, fundus disorders are the primary cause of blindness in humans. Diabetic retino- pathy (DR), glaucoma, cataract, and age-related macular degeneration are the most common ocular illnesses (AMD). According to linked studies, more than 400 million individuals will have DR by 2030 [4]. ese ocular illnesses are becoming a major global health concern. Most significantly, the oph- thalmic illness is incurable, and it might result in permanent blindness. Early identification of these disorders helps avoid vision impairment in clinical circumstances. Nevertheless, there is a major disparity between the number of ophthal- mologists and the number of patients. Furthermore, manually examining the fundus is time-consuming and vastly depen- dent on ophthalmologists’ experience. is complicates large- scale fundus screening. As a result, automated computer-aided Hindawi Computational Intelligence and Neuroscience Volume 2022, Article ID 5007111, 12 pages https://doi.org/10.1155/2022/5007111