AIMC Topic: Fundus Oculi

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A deep convolutional neural network-based novel class balancing for imbalance data segmentation.

Scientific reports
Retinal fundus images provide valuable insights into the human eye's interior structure and crucial features, such as blood vessels, optic disk, macula, and fovea. However, accurate segmentation of retinal blood vessels can be challenging due to imba...

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International ophthalmology
UNLABELLED: Early detection of glaucoma represents a vital factor in securing vision while the disease retains its position as one of the central causes of blindness worldwide. The current glaucoma screening strategies with expert interpretation depe...

Evaluating real-world performance of an automated offline glaucoma AI on a smartphone fundus camera across glaucoma severity stages.

PloS one
PURPOSE: Leveraging an artificial intelligence system (AI) for glaucoma screening can mitigate the current challenges and provide prompt detection and management crucial in averting irreversible blindness. The study reports the real-world performance...

Realistic fundus photograph generation for improving automated disease classification.

The British journal of ophthalmology
AIMS: This study aims to investigate whether denoising diffusion probabilistic models (DDPMs) could generate realistic retinal images, and if they could be used to improve the performance of a deep convolutional neural network (CNN) ensemble for mult...

Research on the correlation between retinal vascular parameters and axial length in children using an AI-based fundus image analysis system.

PloS one
OBJECTIVE: This study aims to utilize artificial intelligence technology to conduct an in-depth analysis of fundus data from myopic children and adolescents, thoroughly exploring the correlation between retinal vascular parameters and axial length (A...

A Lesion-Fusion Neural Network for Multi-View Diabetic Retinopathy Grading.

IEEE journal of biomedical and health informatics
As the most common complication of diabetes, diabetic retinopathy (DR) is one of the main causes of irreversible blindness. Automatic DR grading plays a crucial role in early diagnosis and intervention, reducing the risk of vision loss in people with...

Latent space autoencoder generative adversarial model for retinal image synthesis and vessel segmentation.

BMC medical imaging
Diabetes is a widespread condition that can lead to serious vision problems over time. Timely identification and treatment of diabetic retinopathy (DR) depend on accurately segmenting retinal vessels, which can be achieved through the invasive techni...

A hybrid deep learning framework for early detection of diabetic retinopathy using retinal fundus images.

Scientific reports
Recent advancements in deep learning have significantly impacted medical image processing domain, enabling sophisticated and accurate diagnostic tools. This paper presents a novel hybrid deep learning framework that combines convolutional neural netw...

General retinal image enhancement via reconstruction: Bridging distribution shifts using latent diffusion adaptors.

Medical image analysis
Deep learning-based fundus image enhancement has attracted extensive research attention recently, which has shown remarkable effectiveness in improving the visibility of low-quality images. However, these methods are often constrained to specific dat...

Optimized glaucoma detection using HCCNN with PSO-driven hyperparameter tuning.

Biomedical physics & engineering express
. This study is focused on creating an effective glaucoma detection system employing a Hybrid Centric Convolutional Neural Network (HCCNN) model. By using Particle Swarm Optimization (PSO), classification accuracy is increased and computing complexit...