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Open AccessArticle

Automated Diabetic Retinopathy Screening System Using Hybrid Simulated Annealing and Ensemble Bagging Classifier

1
Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
2
School of Information and Telecommunication Engineering, Tokai University, Tokyo 108-8619, Japan
3
Department of Ophthalmology, Bhumibol Adulyadej Hospital, Royal Thai Air Force, Bangkok 10220, Thailand
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(7), 1198; https://doi.org/10.3390/app8071198
Received: 5 July 2018 / Revised: 18 July 2018 / Accepted: 19 July 2018 / Published: 22 July 2018
(This article belongs to the Special Issue Advanced Intelligent Imaging Technology)
Diabetic Retinopathy (DR) is the leading cause of blindness in working-age adults globally. Primary screening of DR is essential, and it is recommended that diabetes patients undergo this procedure at least once per year to prevent vision loss. However, in addition to the insufficient number of ophthalmologists available, the eye examination itself is labor-intensive and time-consuming. Thus, an automated DR screening method using retinal images is proposed in this paper to reduce the workload of ophthalmologists in the primary screening process and so that ophthalmologists may make effective treatment plans promptly to help prevent patient blindness. First, all possible candidate lesions of DR were segmented from the whole retinal image using a combination of morphological-top-hat and Kirsch edge-detection methods supplemented by pre- and post-processing steps. Then, eight feature extractors were utilized to extract a total of 208 features based on the pixel density of the binary image as well as texture, color, and intensity information for the detected regions. Finally, hybrid simulated annealing was applied to select the optimal feature set to be used as the input to the ensemble bagging classifier. The evaluation results of this proposed method, on a dataset containing 1200 retinal images, indicate that it performs better than previous methods, with an accuracy of 97.08%, a sensitivity of 90.90%, a specificity of 98.92%, a precision of 96.15%, an F-measure of 93.45% and the area under receiver operating characteristic curve at 98.34%. View Full-Text
Keywords: diabetic retinopathy; hybrid simulated annealing; ensemble bagging diabetic retinopathy; hybrid simulated annealing; ensemble bagging
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MDPI and ACS Style

Sreng, S.; Maneerat, N.; Hamamoto, K.; Panjaphongse, R. Automated Diabetic Retinopathy Screening System Using Hybrid Simulated Annealing and Ensemble Bagging Classifier. Appl. Sci. 2018, 8, 1198. https://doi.org/10.3390/app8071198

AMA Style

Sreng S, Maneerat N, Hamamoto K, Panjaphongse R. Automated Diabetic Retinopathy Screening System Using Hybrid Simulated Annealing and Ensemble Bagging Classifier. Applied Sciences. 2018; 8(7):1198. https://doi.org/10.3390/app8071198

Chicago/Turabian Style

Sreng, Syna; Maneerat, Noppadol; Hamamoto, Kazuhiko; Panjaphongse, Ronakorn. 2018. "Automated Diabetic Retinopathy Screening System Using Hybrid Simulated Annealing and Ensemble Bagging Classifier" Appl. Sci. 8, no. 7: 1198. https://doi.org/10.3390/app8071198

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