TY - JOUR
T1 - A hybrid convolutional neural network model for detection of diabetic retinopathy
AU - Alshawabkeh, Musa
AU - Ryalat, Mohammad Hashem
AU - Dorgham, Osama M.
AU - Alkharabsheh, Khalid
AU - Btoush, Mohammad Hjouj
AU - Alazab, Mamoun
PY - 2023/5/13
Y1 - 2023/5/13
N2 - Diabetic retinopathy causes vision loss. Regular eye screening has to be done to provide the appropriate treatment and for vision loss prevention. Globally, patients with DR are increasing, which leads to work pressure on specialists and equipment. Fundus images are a key factor in effective retinal diagnosis. In this paper, a deep-learning approach is proposed to detect DR from retinal images. The proposed approach involves a combination of four effective techniques: image augmentation, contrast limited adaptive histogram equalisation, CNN and transfer learning and ensemble classification. The results show the proposed approach obtained high values of accuracy (93%), precision (95%) and recall (96%), and more stability compared with other approaches.
AB - Diabetic retinopathy causes vision loss. Regular eye screening has to be done to provide the appropriate treatment and for vision loss prevention. Globally, patients with DR are increasing, which leads to work pressure on specialists and equipment. Fundus images are a key factor in effective retinal diagnosis. In this paper, a deep-learning approach is proposed to detect DR from retinal images. The proposed approach involves a combination of four effective techniques: image augmentation, contrast limited adaptive histogram equalisation, CNN and transfer learning and ensemble classification. The results show the proposed approach obtained high values of accuracy (93%), precision (95%) and recall (96%), and more stability compared with other approaches.
KW - convolutional neural networks
KW - deep learning
KW - diabetic retinopathy
KW - ensemble classification
KW - eye diseases
KW - medical applications
KW - retinal diagnosis
KW - retinal images
UR - http://www.scopus.com/inward/record.url?scp=85161852802&partnerID=8YFLogxK
U2 - 10.1504/IJCAT.2022.130886
DO - 10.1504/IJCAT.2022.130886
M3 - Article
AN - SCOPUS:85161852802
SN - 0952-8091
VL - 70
SP - 179
EP - 196
JO - International Journal of Computer Applications in Technology
JF - International Journal of Computer Applications in Technology
IS - 3-4
ER -