Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Author Correction: An efficient enhanced stacked auto encoder assisted optimized deep neural network for forecasting Dry Eye Disease.

Scientific reports·2025
Same author

Deep-ATM DL-LSTM: A novel adaptive thresholding model with dual-layer LSTM architecture for real-time driver drowsiness detection using skin conductance signals.

Computers in biology and medicine·2025
Same author

Performance of Nano-Silica Modified Self-Compacting Glass Mortar at Normal and Elevated Temperatures.

Materials (Basel, Switzerland)·2019
See all related articles

Related Experiment Video

Updated: Jun 9, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

965

An efficient enhanced stacked auto encoder assisted optimized deep neural network for forecasting Dry Eye Disease.

Steffi Rajan1, Suresh Ponnan2

  • 1Department of Electronics and Communication Engineering, Vins Christian College of Engineering, Chunkankadai, Nagercoil, Tamil Nadu, 629502, India. steffirajan7@gmail.com.

Scientific Reports
|October 22, 2024
PubMed
Summary

This study presents a new deep learning model, ESAE-ODNN, for predicting Dry Eye Disease (DED). The model achieves high accuracy in early DED diagnosis by analyzing Meibomian Gland Dysfunction features.

Keywords:
AccuracyChaotic mapClassificationDry eyeMeibomian glandNeural networkOptimization

More Related Videos

A Chronic Autoimmune Dry Eye Rat Model with Increase in Effector Memory T Cells in Eyeball Tissue
09:42

A Chronic Autoimmune Dry Eye Rat Model with Increase in Effector Memory T Cells in Eyeball Tissue

Published on: June 7, 2017

11.4K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K

Related Experiment Videos

Last Updated: Jun 9, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

965
A Chronic Autoimmune Dry Eye Rat Model with Increase in Effector Memory T Cells in Eyeball Tissue
09:42

A Chronic Autoimmune Dry Eye Rat Model with Increase in Effector Memory T Cells in Eyeball Tissue

Published on: June 7, 2017

11.4K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K

Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Meibomian Gland Dysfunction (MGD) and Dry Eye Disease (DED) are prevalent eye conditions affecting millions globally.
  • Early detection and management of DED are crucial for improving patients' Quality of Life (QoL).
  • Existing diagnostic methods may not fully capture the complex etiological factors of DED.

Purpose of the Study:

  • To introduce an advanced deep learning model, the Enhanced Stacked Autoencoder-Optimised Deep Neural Network (ESAE-ODNN), for accurate and early prediction of DED.
  • To enhance the identification and classification of DED by integrating sophisticated feature selection and extraction techniques.
  • To improve the efficiency and accuracy of DED diagnosis through novel optimization strategies.

Main Methods:

  • The ESAE-ODNN model utilizes feature selection (FS) incorporating chaotic maps and feature extraction (FE) via a stacked autoencoder (ESAE) to identify critical MGD-related features.
  • A deep neural network (ODNN) classifier is employed for DED prediction, optimized using the Enhanced Quantum Bacterial Foraging Optimisation Algorithm (EQBFOA).
  • The model incorporates SLSTM-STSA for enhanced classification accuracy and minimizes irrelevant/redundant features for robust performance.

Main Results:

  • The proposed ESAE-ODNN model achieved a high classification accuracy of 96.34% in predicting DED.
  • The method demonstrated efficiency in accurate identification, reduced computational complexity, and fine-tuned performance.
  • Experimental evaluations confirmed the model's robustness in handling intricate features and high-dimensional data.

Conclusions:

  • The ESAE-ODNN model offers a novel and efficient approach for the early diagnosis of DED.
  • The integration of deep learning with advanced optimization techniques significantly improves the understanding and classification of MGD features.
  • The proposed method outperforms existing state-of-the-art techniques in DED prediction, offering a promising tool for clinical application.