Image Preprocessing in Classification and Identification of Diabetic Eye Diseases

Data Science and Engineering - Tập 6 Số 4 - Trang 455-471 - 2021
Rubina Sarki1, Khandakar Ahmed1, Hua Wang1, Yanchun Zhang1, Jiangang Ma2, Kate Wang3
1Victoria University, Melbourne, Australia
2Federation University, Mount Helen, Australia
3RMIT University, Melbourne, Australia

Tóm tắt

AbstractDiabetic 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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