Open Access
ARTICLE
Deep Learning with a Novel Concoction Loss Function for Identification of Ophthalmic Disease
Sayyid Kamran Hussain1, Ali Haider Khan2,*, Malek Alrashidi3, Sajid Iqbal4, Qazi Mudassar Ilyas4, Kamran Shah5
1 Department of Computer Science, TIMES Institute, Multan, 60000, Pakistan
2 Department of Software Engineering, Faculty of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan
3 Department of Computer Science, Applied College, University of Tabuk, Tabuk, Saudi Arabia
4 Department of Information Systems, College of Computer Sciences and Information Technology, King Faisal University,
Al Hofuf, 31982, Saudi Arabia
5 Department of Mechanical Engineering, College of Engineering, King Faisal University, Al Hofuf, Saudi Arabia
* Corresponding Author: Ali Haider Khan. Email:
(This article belongs to the Special Issue: Recent Advances in Ophthalmic Diseases Diagnosis using AI)
Computers, Materials & Continua 2023, 76(3), 3763-3781. https://doi.org/10.32604/cmc.2023.041722
Received 03 May 2023; Accepted 21 July 2023; Issue published 08 October 2023
Abstract
As ocular computer-aided diagnostic (CAD) tools become more widely accessible, many researchers are developing
deep learning (DL) methods to aid in ocular disease (OHD) diagnosis. Common eye diseases like cataracts (CATR),
glaucoma (GLU), and age-related macular degeneration (AMD) are the focus of this study, which uses DL to
examine their identification. Data imbalance and outliers are widespread in fundus images, which can make it
difficult to apply many DL algorithms to accomplish this analytical assignment. The creation of effcient and reliable
DL algorithms is seen to be the key to further enhancing detection performance. Using the analysis of images of
the color of the retinal fundus, this study offers a DL model that is combined with a one-of-a-kind concoction loss
function (CLF) for the automated identification of OHD. This study presents a combination of focal loss (FL) and
correntropy-induced loss functions (CILF) in the proposed DL model to improve the recognition performance of
classification performance of the DL
model with our proposed loss function is compared to that of the baseline models using accuracy (ACU), recall
(REC), specificity (SPF), Kappa, and area under the receiver operating characteristic curve (AUC) as the evaluation
metrics. The testing shows that the method is reliable and effcient.
Keywords
Cite This Article
APA Style
Hussain, S.K., Khan, A.H., Alrashidi, M., Iqbal, S., Ilyas, Q.M. et al. (2023). Deep learning with a novel concoction loss function for identification of ophthalmic disease. Computers, Materials & Continua, 76(3), 3763-3781. https://doi.org/10.32604/cmc.2023.041722
Vancouver Style
Hussain SK, Khan AH, Alrashidi M, Iqbal S, Ilyas QM, Shah K. Deep learning with a novel concoction loss function for identification of ophthalmic disease. Comput Mater Contin. 2023;76(3):3763-3781 https://doi.org/10.32604/cmc.2023.041722
IEEE Style
S.K. Hussain, A.H. Khan, M. Alrashidi, S. Iqbal, Q.M. Ilyas, and K. Shah "Deep Learning with a Novel Concoction Loss Function for Identification of Ophthalmic Disease," Comput. Mater. Contin., vol. 76, no. 3, pp. 3763-3781. 2023. https://doi.org/10.32604/cmc.2023.041722