Automated Diabetic Retinopathy Screening System Using Hybrid Simulated Annealing and Ensemble Bagging Classifier  
[PERSON]  
1 Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand; [EMAIL_ADDRESS]  
2 School of Information and Telecommunication Engineering, Tokai University, Tokyo 108-8619, Japan; [EMAIL_ADDRESS]  
3 Department of Ophthalmology, Bhumbiol Adulyadej Hospital, Royal Thai Air Force, Bangkok 10220, Thailand; [EMAIL_ADDRESS]  
4 Correspondence: [EMAIL_ADDRESS]; Tel. : +66-61-865-0252  
November 6, 2021 November 6, 20  
EXs are MAs exuded combined with the leakage of lipids in the retina. They appear as pale white or fluffy patches (soft EXs) or bright yellowish dots (hard EXs) in the inner retina. Neovascularization is the development of immature blood vessels which usually occur as a result of generalized retinal ischemia. Vitreous hemorrhage is the result of hemorrhage or blood bleeding into the vitreous humor, and fibrous proliferation is fibrous tissue opaque enough to be seen at the Optic Disc (OD) area or somewhere around the OD margin with or without accompanying new blood vessels. OD, macula, and Blood Vessels (BV) are normal retinal structures. The anatomy of a healthy retinal image and the pathological signs of DR images are shown in Figures 1 and 2. As reported in the International Diabetes Federation Diabetes Atlas 2017 [2], an estimated 425 million adults were living with diabetes as of 2017, and approximately one-third of diabetes patients had some degree of diabetic retinopathy. Consequently, it is recommended that people with diabetes undergo eye-screening at regular yearly intervals to help prevent vision loss. This eye screening is currently labor-intensive, time-consuming, as well as subjective and can be performed by a retina specialist either directly or by first using a fundus camera to capture the retinal image, followed by a screening process [3]. However, the number of ophthalmologists available is insufficient in many countries, especially in developing countries and rural areas where eye specialists are rarely available. To overcome these limitations, this paper proposes an automatic DR screening system to identify DR more objectively and enable ophthalmologists to make effective treatment plans in order to help prevent patient blindness. In our review of previous works, we found that several papers aimed to detect each pathological sign of DR: EXs, MAs, and HMs. However, only a few papers were devoted to differentiating between healthy and unhealthy retinal images. Figure 1: Healthy fundus image and diabetic retinopathy image. [PERSON] et al. [4] proposed an automated screening system to discriminate healthy retinal images using a support vector machine for three different eye diseases namely DR, glaucoma and age-related macular degeneration. First, an image was preprocessed using adaptive-histogram equalization, and then a bi-dimensional empirical mode decomposition technique was applied to decompose the image due to pixel variations. Texture features were later extracted from 2D intrinsic mode functions and ranked as inputs for feeding the support vector machine. The paper reported an accuracy of 88.63%, a sensitivity of 86.25% and a specificity of 91%. The improved version of this paper was published in [5]. A 2D-continuous wavelet transform was used first to decompose the fundus images, and then 15 features from entropies and energy features were extracted. These significant features were ranked and selected using particle swarm optimization. Finally, an adaptive synthetic sampling approach was applied to balance the input data which was then differentiated into two data classes using a random forest classifier. The updated results showed an accuracy of 92.48%, a sensitivity of 89.37% and a specificity of 95.58%. [PERSON] et al. [6] classified DR or healthy images from retinal images using the aggregate of the extracted lesions. The OD and BV were first detected.