Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

The Retina

The Retina

The retina is a layer of nervous tissue at the back of the eye that transduces light into neural signals. This process, called phototransduction, is carried out by rod and cone photoreceptor cells in the back of the retina.

Vision

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

Visual System

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...

Photoreceptors and Visual Pathways

Photoreceptors and Visual Pathways

At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category,...

Anatomy of the Eyeball

Anatomy of the Eyeball

The eye is a spherical, hollow structure composed of three tissue layers. The outer layer — the fibrous tunic, comprises the sclera — a white structure — and the cornea, which is transparent. The sclera encompasses some of the ocular surface, most of which is not visible. However, the 'white of the eye' is distinctively visible in humans compared to other species. The cornea, a clear covering at the front of the eye, enables light penetration. The eye's middle...

You might also read

Related Articles

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

Sort by

Same author

Dynamic driver drowsiness detection with attention enhanced convolutional neural networks for real time monitoring and road safety applications.

Scientific reports·2026

Same author

A hybrid transformer-zero-shot learning framework with Muon optimization for intelligent channel estimation in MIMO wireless systems.

Scientific reports·2026

Same author

High performance IP lookup through GPU acceleration to support scalable and efficient routing in data driven communication networks.

Scientific reports·2026

Same author

A Histopathological Study of Induced Open Wounds Treated With Urinary Bladder Submucosa Scaffold in Rabbits.

Veterinary medicine international·2026

Same author

High-precision crop recommendation system with stacking ensemble classifiers for optimizing agricultural productivity.

Scientific reports·2025

Same author

B-UMCS: Blockchain-enabled Unified Medical Consultancy Service.

PloS one·2024

Search research articles

Related Experiment Video

Updated: Nov 24, 2025

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Classification of retinal images based on convolutional neural network.

Noha A El-Hag¹, Ahmed Sedik², Walid El-Shafai³

¹Dept. of Electronics and Electrical Comm., Faculty of Engineering, Minia University, Minya, Egypt.

Microscopy Research and Technique

|December 22, 2020

Summary

This summary is machine-generated.

This study introduces two frameworks for automatic maculopathy detection from retinal images. Early detection of diabetic maculopathy is crucial for timely patient treatment and improved outcomes.

Keywords:

CNN Gaussian gradient contrast enhancement diabetic maculopathy exudate detection fuzzy enhancement retinal images segmentation process

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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

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

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Nov 24, 2025

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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

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

Published on: November 30, 2022

Area of Science:

Ophthalmology
Medical Imaging
Computer Vision

Background:

Diabetic maculopathy diagnosis requires manual effort from ophthalmologists.
Early detection of maculopathy is vital for effective patient treatment.
Exudate detection in retinal images aids maculopathy diagnosis.

Purpose of the Study:

To develop automated frameworks for accurate maculopathy disease detection.
To assist ophthalmologists in the early diagnosis of diabetic maculopathy.
To improve the efficiency and accuracy of maculopathy screening.

Main Methods:

Framework 1: Fuzzy preprocessing, image segmentation (binarization), removal of optic disc and blood vessels, gradient processing, and cumulative histogram analysis.
Framework 2: Utilization of a Convolutional Neural Network (CNN) for image classification.
Both methods aim to classify retinal images as normal or abnormal.

Main Results:

The proposed frameworks enable automatic classification of retinal images.
The methods facilitate the discrimination between normal and abnormal cases for maculopathy detection.
The study presents novel approaches for enhancing contrast and extracting relevant features from retinal images.

Conclusions:

Automated detection systems can significantly aid ophthalmologists in diagnosing maculopathy.
The developed frameworks offer efficient and accurate tools for early maculopathy detection.
Further research can refine these methods for broader clinical application.