Open Access

ARTICLE

A Novel Light Weight CNN Framework Integrated with Marine Predator Optimization for the Assessment of Tear Film-Lipid Layer Patterns

by Bejoy Abraham¹, Jesna Mohan², Linu Shine³, Sivakumar Ramachandran^3,*

1 Department of Computer Science and Engineering, College of Engineering Muttathara, Thiruvananthapuram, Kerala, 695008, India
2 Department of Computer Science and Engineering, Mar Baselios College of Engineering and Technology, Thiruvananthapuram, Kerala, 695015, India
3 Department of Electronics and Communication Engineering, College of Engineering Trivandrum, Kerala, 695016, India

* Corresponding Author: Sivakumar Ramachandran. Email:

Computer Modeling in Engineering & Sciences 2023, 136(1), 87-106. https://doi.org/10.32604/cmes.2023.023384

Received 23 April 2022; Accepted 02 September 2022; Issue published 05 January 2023

Abstract

Tear film, the outermost layer of the eye, is a complex and dynamic structure responsible for tear production. The tear film lipid layer is a vital component of the tear film that provides a smooth optical surface for the cornea and wetting the ocular surface. Dry eye syndrome (DES) is a symptomatic disease caused by reduced tear production, poor tear quality, or excessive evaporation. Its diagnosis is a difficult task due to its multifactorial etiology. Out of several clinical tests available, the evaluation of the interference patterns of the tear film lipid layer forms a potential tool for DES diagnosis. An instrument known as Tearscope Plus allows the rapid assessment of the lipid layer. A grading scale composed of five categories is used to classify lipid layer patterns. The reported work proposes the design of an automatic system employing light weight convolutional neural networks (CNN) and nature inspired optimization techniques to assess the tear film lipid layer patterns by interpreting the images acquired with the Tearscope Plus. The designed framework achieves promising results compared with the existing state-of-the-art techniques.

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Keywords

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