@Article{cmc.2023.041722,
AUTHOR = {Sayyid Kamran Hussain, Ali Haider Khan, Malek Alrashidi, Sajid Iqbal, Qazi Mudassar Ilyas, Kamran Shah},
TITLE = {Deep Learning with a Novel Concoction Loss Function for Identification of Ophthalmic Disease},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {76},
YEAR = {2023},
NUMBER = {3},
PAGES = {3763--3781},
URL = {http://www.techscience.com/cmc/v76n3/54315},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2023.041722}
}