(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No. 12, 2017 369 | Page www.ijacsa.thesai.org A Decision Support System for Early-Stage Diabetic Retinopathy Lesions Kemal AKYOL Computer Engineering Department Kastamonu University Kastamonu, Turkey Şafak BAYIR Computer Engineering Department Karabuk University Karabuk, Turkey Baha ŞEN Computer Engineering Department Yildirim Beyazit University Ankara, Turkey Abstract—Retina is a network layer containing light-sensitive cells. Diseases that occur in this layer, which performs the eye- sight, threaten our eye-sight directly. Diabetic Retinopathy is one of the main complications of diabetes mellitus and it is the most significant factor contributing to blindness in the later stages of the disease. Therefore, early diagnosis is of great importance to prevent the progress of this disease. For this purpose, in this study, an application based on image processing techniques and machine learning, which provides decision support to specialist, was developed for the detection of hard exudates, cotton spots, hemorrhage and microaneurysm lesions which appear in the early stages of the disease. The meaningful information was extracted from a set of samples obtained from the DIARETDB1 dataset during the system modeling process. In this process, Gabor and Discrete Fourier Transform attributes were utilized and dimension reduction was performed by using Spectral Regression Discriminant Analysis algorithm. Then, Random Forest and Logistic Regression and classifier algorithms’ performances were evaluated on each attribute dataset. Experimental results were obtained using the retinal fundus images provided from both DIARETDB1 dataset and the department of Ophthalmology, Ataturk Training and Research Hospital in Ankara. Keywords—Early stage diabetic retinopathy lesions; feature extraction; important features; image recognition; classification; decision support system; computer aided analysis I. INTRODUCTION Diabetic Retinopathy (DR) which is the subject of many studies in medical image processing field is a disease that begins with the influence of the retinal capillaries due to effect of blood sugar increase depending on diabetes and can result complete loss of sense of sight in its progressive stages [1]-[2]. There are two phases of DR disease which is directly proportional with the level of structural deterioration in retinal images: early-stage diabetic retinopathy (ESDR) and advanced-stage diabetic retinopathy (ASDR) [3]. Clogging of network layer vessels, small vessel dilatations, intraretinal hemorrhages and yellow deposits called hard exudate are seen at the onset of this disease [2], [4]. Lesion samples which occur in the early stage of this disease are given in Fig. 1. Automatic detection and segmentation studies on ESDR disease gained great momentum in recent years, furthermore, new competencies are being added each passing day. In addition, the regular examination requirement of this disease and the lack of specialist make the procedures that should be carried out with automated systems compulsory. The aim of this study is to investigate the methodology and techniques that will enable us to detect accurately the location of the structural disorders namely lesions occur in the early stage of DR and to model the decision support system that gives the most accurate result. The application of ESDR lesions‟ detection which is based on literature reviews and reference to the tissue classification approach, involves the construction of the model and the analysis of new retinal images basically. This application includes the interfaces that are useful for field specialists in the decision of improving their cognitive abilities related to understanding and comprehension. Contribution: There are many studies on the detection of ESDR lesions in the relevant literature. This study shows similarities with other studies in terms of workflow, but it differs from others using Discrete Fourier Transform Attributes (DFTA) and Spectral Regression Discriminant Analysis (SRDA) algorithms. On the other hand, the saving ability for the ESDR lesions position in retinal images which are taken at different dates of a patient, allows the field specialist to instantly compare the patient‟s previous recordings in the system, thereby allows specialist in order to examine the development of disease in particular date and time intervals. Fig. 1. ESDR structural disorders, a) microaneurysm, b) hemorrhage, c) hard exudate, d) cotton spots [5].