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Automatic Retinal Image Analysis for the Detection of Diabetic Retinopathy

Automatic Retinal Image Analysis for the Detection of Diabetic Retinopathy

Prasanna Porwal, Samiksha Pachade, Manesh Kokare, Girish Deshmukh, Vivek Sahasrabuddhe
Copyright: © 2018 |Pages: 16
ISBN13: 9781522528296|ISBN10: 1522528296|EISBN13: 9781522528302
DOI: 10.4018/978-1-5225-2829-6.ch008
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MLA

Porwal, Prasanna, et al. "Automatic Retinal Image Analysis for the Detection of Diabetic Retinopathy." Biomedical Signal and Image Processing in Patient Care, edited by Maheshkumar H. Kolekar and Vinod Kumar, IGI Global, 2018, pp. 146-161. https://doi.org/10.4018/978-1-5225-2829-6.ch008

APA

Porwal, P., Pachade, S., Kokare, M., Deshmukh, G., & Sahasrabuddhe, V. (2018). Automatic Retinal Image Analysis for the Detection of Diabetic Retinopathy. In M. Kolekar & V. Kumar (Eds.), Biomedical Signal and Image Processing in Patient Care (pp. 146-161). IGI Global. https://doi.org/10.4018/978-1-5225-2829-6.ch008

Chicago

Porwal, Prasanna, et al. "Automatic Retinal Image Analysis for the Detection of Diabetic Retinopathy." In Biomedical Signal and Image Processing in Patient Care, edited by Maheshkumar H. Kolekar and Vinod Kumar, 146-161. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-2829-6.ch008

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Abstract

Diabetic Retinopathy, a condition in the person affected by diabetes, is most common cause of blindness in the world. Recent research has given a better understanding of requirement in clinical eye care practice to identify better and cheaper ways of identification, management, diagnosis and treatment of retinal disease. The importance of diabetic retinopathy screening programs and difficulty in achieving reliable early diagnosis of diabetic retinopathy at a reasonable cost needs attention to develop computer-aided diagnosis tool. Computer aided disease diagnosis in retinal image analysis could ease mass screening of population with diabetes mellitus and help clinicians in utilizing their time more efficiently. The recent technological advances in computing power, communication systems, and machine learning techniques provide opportunities to the biomedical engineers and computer scientists to meet the requirements of clinical practice. With proper self-care, management, and medical professional support, individuals with diabetes can live a healthy and long life.

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