Image Preprocessing in Classification and Identification of Diabetic Eye Diseases

Diabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in earl...

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Published in:	Data Science and Engineering Vol. 6; no. 4; pp. 455 - 471
Main Authors:	Sarki, Rubina, Ahmed, Khandakar, Wang, Hua, Zhang, Yanchun, Ma, Jiangang, Wang, Kate
Format:	Journal Article
Language:	English
Published:	Singapore Springer Singapore 01.12.2021 Springer Springer Nature B.V
Subjects:	Algorithm Analysis and Problem Complexity Artificial Intelligence Artificial neural networks Cable television broadcasting industry Chemistry and Earth Sciences Classification Computer Science Data Mining and Knowledge Discovery Database Management Diabetes Diabetics Diagnosis Eye diseases Geometric transformation Image classification Image enhancement Image processing Image quality Image segmentation Medical imaging Medical imaging equipment Neural networks Physics Statistics for Engineering Systems and Data Security United States Image processing Convolution neural network Diabetic eye disease
ISSN:	2364-1185, 2364-1541, 2364-1541
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Abstract	Diabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model’s development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity.
AbstractList	Diabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model's development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity.Diabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model's development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity. Diabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model’s development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity.
Audience	Academic
Author	Ahmed, Khandakar Ma, Jiangang Wang, Kate Wang, Hua Sarki, Rubina Zhang, Yanchun
Author_xml	– sequence: 1 givenname: Rubina surname: Sarki fullname: Sarki, Rubina organization: Victoria University – sequence: 2 givenname: Khandakar surname: Ahmed fullname: Ahmed, Khandakar organization: Victoria University – sequence: 3 givenname: Hua surname: Wang fullname: Wang, Hua organization: Victoria University – sequence: 4 givenname: Yanchun surname: Zhang fullname: Zhang, Yanchun organization: Victoria University – sequence: 5 givenname: Jiangang orcidid: 0000-0002-8449-7610 surname: Ma fullname: Ma, Jiangang email: j.ma@federation.edu.au organization: Federation University – sequence: 6 givenname: Kate surname: Wang fullname: Wang, Kate organization: RMIT University
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/34423109$$D View this record in MEDLINE/PubMed
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Keywords	Image processing Convolution neural network Diabetic eye disease
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Snippet	Diabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because...
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SubjectTerms	Algorithm Analysis and Problem Complexity Artificial Intelligence Artificial neural networks Cable television broadcasting industry Chemistry and Earth Sciences Classification Computer Science Data Mining and Knowledge Discovery Database Management Diabetes Diabetics Diagnosis Eye diseases Geometric transformation Image classification Image enhancement Image processing Image quality Image segmentation Medical imaging Medical imaging equipment Neural networks Physics Statistics for Engineering Systems and Data Security
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Title	Image Preprocessing in Classification and Identification of Diabetic Eye Diseases
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