Current advances and future perspectives of image fusion: A comprehensive review

S Karim, G Tong, J Li, A Qadir, U Farooq, Y Yu - Information Fusion, 2023 - Elsevier
Multiple imaging modalities can be combined to provide more information about the real
world than a single modality alone. Infrared images discriminate targets with respect to their …

[HTML][HTML] Artificial intelligence in retina

U Schmidt-Erfurth, A Sadeghipour, BS Gerendas… - Progress in retinal and …, 2018 - Elsevier
Major advances in diagnostic technologies are offering unprecedented insight into the
condition of the retina and beyond ocular disease. Digital images providing millions of …

An overview of artificial intelligence in diabetic retinopathy and other ocular diseases

B Sheng, X Chen, T Li, T Ma, Y Yang, L Bi… - Frontiers in Public …, 2022 - frontiersin.org
Artificial intelligence (AI), also known as machine intelligence, is a branch of science that
empowers machines using human intelligence. AI refers to the technology of rendering …

Deep learning for diabetes: a systematic review

T Zhu, K Li, P Herrero… - IEEE Journal of Biomedical …, 2020 - ieeexplore.ieee.org
Diabetes is a chronic metabolic disorder that affects an estimated 463 million people
worldwide. Aiming to improve the treatment of people with diabetes, digital health has been …

Classification of diabetic retinopathy: Past, present and future

Z Yang, TE Tan, Y Shao, TY Wong, X Li - Frontiers in Endocrinology, 2022 - frontiersin.org
Diabetic retinopathy (DR) is a leading cause of visual impairment and blindness worldwide.
Since DR was first recognized as an important complication of diabetes, there have been …

Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning

M Treder, JL Lauermann, N Eter - Graefe's Archive for Clinical and …, 2018 - Springer
Purpose Our purpose was to use deep learning for the automated detection of age-related
macular degeneration (AMD) in spectral domain optical coherence tomography (SD-OCT) …

A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography

G Ryu, K Lee, D Park, SH Park, M Sagong - Scientific reports, 2021 - nature.com
As the prevalence of diabetes increases, millions of people need to be screened for diabetic
retinopathy (DR). Remarkable advances in technology have made it possible to use artificial …

Deep learning-based automated classification of multi-categorical abnormalities from optical coherence tomography images

W Lu, Y Tong, Y Yu, Y Xing, C Chen… - … vision science & …, 2018 - tvst.arvojournals.org
Purpose: To develop a new intelligent system based on deep learning for automatically
optical coherence tomography (OCT) images categorization. Methods: A total of 60,407 OCT …

Application of machine learning in ophthalmic imaging modalities

Y Tong, W Lu, Y Yu, Y Shen - Eye and Vision, 2020 - Springer
In clinical ophthalmology, a variety of image-related diagnostic techniques have begun to
offer unprecedented insights into eye diseases based on morphological datasets with …

Transfer learning-based model for diabetic retinopathy diagnosis using retinal images

MK Jabbar, J Yan, H Xu, Z Ur Rehman, A Jabbar - Brain Sciences, 2022 - mdpi.com
Diabetic retinopathy (DR) is a visual obstacle caused by diabetic disease, which forms
because of long-standing diabetes mellitus, which damages the retinal blood vessels. This …