Open Access

ARTICLE

Deep Learning with a Novel Concoction Loss Function for Identification of Ophthalmic Disease

by Sayyid Kamran Hussain¹, Ali Haider Khan^2,*, 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.

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Keywords

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