Authors:
Arun Kumar Yadav
1
;
Arti Jain
2
;
Jorge Luis Morato Lara
3
and
Divakar Yadav
1
Affiliations:
1
Department of Computer Science & Engineering, NIT Hamirpur, Himachal Pradesh, India
;
2
Department of Computer Science & Engineering, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India
;
3
Department of Computer Science, Universidad Carlos III de Madrid, Leganes, Madrid, Spain
Keyword(s):
Blood Vessel Segmentation, Convolutional Neural Networks, CLAHE, Diabetic Patients, Retinal Images.
Abstract:
Human beings often become victims to numerous diseases. Among these, diabetes stands out for its impairment of quality of life and even potential mortality. The diabetes needs to be properly taken care of, otherwise failure to detect its presence within proper time duration leads to a loss of life. According to the World Health Organization, the worldwide number of diabetic patients were 463 million during 2019 and is expected to cross 700 million by the 2045i. In the past, a lot of research has been carried out for retinal blood vessel segmentation for identification of Diabetic Retinopathy using various machine learning and deep learning models. In this research work, Convolutional Neural Network (CNN) and CLAHE are applied to tackle the problem of retinal blood vessel segmentation. Experimental evaluation shows that the proposed method outperforms with 0.9806 accuracy, quite competitive with respect to the state-of-art.