Image preprocessing in classification and identification of diabetic eye diseases

[thumbnail of s41019-021-00167-z.pdf]
s41019-021-00167-z.pdf - Published Version (2MB) | Preview
Available under license: Creative Commons Attribution

Sarki, Rubina ORCID: 0000-0001-5018-9567, Ahmed, Khandakar ORCID: 0000-0003-1043-2029, Wang, Hua ORCID: 0000-0002-8465-0996, Zhang, Yanchun ORCID: 0000-0002-5094-5980, Ma, Jiangang ORCID: 0000-0002-8449-7610 and Wang, Kate ORCID: 0000-0001-5208-1090 (2021) Image preprocessing in classification and identification of diabetic eye diseases. Data Science and Engineering, 6. pp. 455-471. ISSN 2364-1185


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.

Dimensions Badge

Altmetric Badge

Item type Article
DOI 10.1007/s41019-021-00167-z
Official URL
Subjects Current > FOR (2020) Classification > 4203 Health services and systems
Current > FOR (2020) Classification > 4602 Artificial intelligence
Current > FOR (2020) Classification > 4611 Machine learning
Current > Division/Research > College of Science and Engineering
Keywords diabetes, diabetic eye disease, DED, diabetic patience, DED treatment, image segmentation, convolution neural network, CNN
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login