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

GlioSurvQNet: A DuelContextAttn DQN Framework for Brain Tumor Prognosis with Metaheuristic Optimization.

Diagnostics (Basel, Switzerland)·2025
Same author

Chest X ray and cough sample based deep learning framework for accurate diagnosis of COVID-19.

Computers & electrical engineering : an international journal·2022
Same author

Brain signatures based on structural MRI: Classification for MCI, PMCI, and AD.

Human brain mapping·2022
Same journal

Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

Diagnostics (Basel, Switzerland)·2026
Same journal

Association Between Systemic Inflammatory Response Biomarkers and Disease Activity in Systemic Lupus Erythematosus: A Multi-Center Retrospective Study.

Diagnostics (Basel, Switzerland)·2026
Same journal

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)·2026
Same journal

Multivalvular Carcinoid Heart Disease: The Role of Echocardiography in Diagnosis and Selection for Heterotopic Bicaval Valve Implantation.

Diagnostics (Basel, Switzerland)·2026
Same journal

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.

Diagnostics (Basel, Switzerland)·2026
Same journal

Anomalous Left Coronary Artery from the Pulmonary Artery: Cinematic Volume Rendering Technique for Enhanced Anatomic Visualization.

Diagnostics (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jun 25, 2025

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

CSDNet: A Novel Deep Learning Framework for Improved Cataract State Detection.

Lahari P L1, Ramesh Vaddi1, Mahmoud O Elish2,3

  • 1Department of Electronics and Communication Engineering, SRM University AP, Andhra Pradesh, India.

Diagnostics (Basel, Switzerland)
|May 24, 2024
PubMed
Summary
This summary is machine-generated.

A new deep learning model, CSDNet, efficiently detects cataract states with high accuracy. This lightweight framework is ideal for real-time applications and devices with limited resources.

Keywords:
cataractclassificationdetectionpre-trained convolutional neural networksvisual impairment

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

520
Author Spotlight: Unraveling the Molecular Mechanisms in PCO and Fibrosis Following Cataract Surgery
05:19

Author Spotlight: Unraveling the Molecular Mechanisms in PCO and Fibrosis Following Cataract Surgery

Published on: December 1, 2023

1.0K

Related Experiment Videos

Last Updated: Jun 25, 2025

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
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

520
Author Spotlight: Unraveling the Molecular Mechanisms in PCO and Fibrosis Following Cataract Surgery
05:19

Author Spotlight: Unraveling the Molecular Mechanisms in PCO and Fibrosis Following Cataract Surgery

Published on: December 1, 2023

1.0K

Area of Science:

  • Ophthalmology
  • Computer Science
  • Artificial Intelligence

Background:

  • Cataracts are a leading cause of vision impairment and blindness globally.
  • Current diagnostic methods face challenges in speed and accessibility.
  • Deep learning offers potential for improved automated detection.

Purpose of the Study:

  • To develop a lightweight and adaptable deep learning framework for cataract state detection.
  • To reduce computational costs and enable real-time or near-real-time inference.
  • To improve the accuracy and efficiency of cataract diagnosis.

Main Methods:

  • Utilized the Ocular Disease Intelligent Recognition (ODIR) database for training and testing.
  • Developed the Cataract States Detection Network (CSDNet) with smaller kernels and fewer parameters.
  • Compared CSDNet's performance against established models like VGG19, ResNet50, and EfficientNet B0.

Main Results:

  • Achieved 97.24% binary classification accuracy (normal vs. cataract).
  • Attained 98.17% accuracy in detecting four cataract states.
  • CSDNet is a lightweight 17 MB model with 175,617 trainable parameters and a 212 ms runtime.

Conclusions:

  • CSDNet provides a highly accurate and efficient solution for cataract detection.
  • The model's lightweight design makes it suitable for resource-constrained environments.
  • CSDNet is well-suited for real-time ophthalmic screening and diagnosis.