Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model

Sensors

Amir Hussain¹  Junaid Rashid²  Jungeun Kim²  Momina Masood³  Awais Mehmood³  Farooq Ali³  Tahira Nazir³  Marriam Nawaz³  Rabbia Mahum³  Hyuk-Yoon Kwon⁴ 

[1] Centre of AI and Data Science, Edinburgh Napier University, Edinburgh EH11 4DY, UK;Department of Computer Science and Engineering, Kongju National University, Gongju 31080, Chungcheongnam-do, Korea;Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan;Research Center for Electrical and Information Technology, Department of Industrial Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea;

关键词: diabetic retinopathy;    diabetic macular edema;    medical imaging;    deep learning;    retinal images;   

DOI  :  10.3390/s21165283

来源: DOAJ

【 摘 要 】

Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person suffering from diabetes. Diabetic macular edema (DME) occurs when DR affects the macula, which causes fluid accumulation in the macula. Efficient screening systems require experts to manually analyze images to recognize diseases. However, due to the challenging nature of the screening method and lack of trained human resources, devising effective screening-oriented treatment is an expensive task. Automated systems are trying to cope with these challenges; however, these methods do not generalize well to multiple diseases and real-world scenarios. To solve the aforementioned issues, we propose a new method comprising two main steps. The first involves dataset preparation and feature extraction and the other relates to improving a custom deep learning based CenterNet model trained for eye disease classification. Initially, we generate annotations for suspected samples to locate the precise region of interest, while the other part of the proposed solution trains the Center Net model over annotated images. Specifically, we use DenseNet-100 as a feature extraction method on which the one-stage detector, CenterNet, is employed to localize and classify the disease lesions. We evaluated our method over challenging datasets, namely, APTOS-2019 and IDRiD, and attained average accuracy of 97.93% and 98.10%, respectively. We also performed cross-dataset validation with benchmark EYEPACS and Diaretdb1 datasets. Both qualitative and quantitative results demonstrate that our proposed approach outperforms state-of-the-art methods due to more effective localization power of CenterNet, as it can easily recognize small lesions and deal with over-fitted training data. Our proposed framework is proficient in correctly locating and classifying disease lesions. In comparison to existing DR and DME classification approaches, our method can extract representative key points from low-intensity and noisy images and accurately classify them. Hence our approach can play an important role in automated detection and recognition of DR and DME lesions.

【 授权许可】

Unknown