Image Preprocessing in Classification and Identification of Diabetic Eye Diseases

• 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
• DOI: 10.1007/s41019-021-00167-z

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.