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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Veröffentlicht in:Data Science and Engineering Jg. 6; H. 4; S. 455 - 471
Hauptverfasser: Sarki, Rubina, Ahmed, Khandakar, Wang, Hua, Zhang, Yanchun, Ma, Jiangang, Wang, Kate
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Singapore 01.12.2021
Springer
Springer Nature B.V
Schlagworte:
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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Zusammenfassung: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.
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ISSN:2364-1185
2364-1541
2364-1541
DOI:10.1007/s41019-021-00167-z