4.6 Article

Detection and classification of red lesions from retinal images for diabetic retinopathy detection using deep learning models

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Information Systems

A survey on recent developments in diabetic retinopathy detection through integration of deep learning

Shalini Agarwal et al.

Summary: This paper investigates recent frameworks based on machine learning and deep learning networks for classifying diabetic retinopathy. With advancements in AI techniques, an efficient and accurate system can aid in early diagnosis and treatment. However, the imbalance in available datasets poses a challenge that needs to be addressed.

MULTIMEDIA TOOLS AND APPLICATIONS (2023)

Article Computer Science, Information Systems

Performance analysis of multi-level thresholding for microaneurysm detection

Kar Heng Choong et al.

Summary: Diabetic retinopathy (DR) is a major cause of blindness among individuals aged 20-74. Early detection and treatment through manual screening by qualified physicians can prevent 90% of cases. While automated screening systems based on image processing have been developed, their accuracy and consistency are still a concern, with manual screening being the preferred option. This paper focuses on analyzing the accuracy and consistency of microaneurysm (MA) detection using Otsu's multi-thresholding in image processing. The study includes analysis using synthetic retinal images under different DR stages, retinal and image parameters, and verification using real retinal images.

MULTIMEDIA TOOLS AND APPLICATIONS (2022)

Article Computer Science, Information Systems

An enhanced swarm optimization-based deep neural network for diabetic retinopathy classification in fundus images

A. Mary Dayana et al.

Summary: Diabetic Retinopathy (DR) is a long-lasting diabetic retinal disorder that can eventually lead to blindness. Classifying the severity of DR has been a challenging task due to the difficulty in analyzing lesion features. This paper proposes an efficient deep neural network with Chronological Tunicate Swarm Algorithm (CTSA) for classifying the severity of DR. The experimental results demonstrate the effectiveness and robustness of the proposed method in the DR classification task.

MULTIMEDIA TOOLS AND APPLICATIONS (2022)

Article Computer Science, Information Systems

Red lesion in fundus image with hexagonal pattern feature and two-level segmentation

D. Latha et al.

Summary: This study proposes a red lesion detection algorithm that uses Hexagonal pattern-based features with two-level segmentation for efficient and accurate identification of early-stage red lesions in diabetic retinopathy. The evaluation results on four datasets demonstrate high accuracy and reliability of the algorithm in terms of various metrics.

MULTIMEDIA TOOLS AND APPLICATIONS (2022)

Article Health Care Sciences & Services

Three-Dimensional Semantic Segmentation of Diabetic Retinopathy Lesions and Grading Using Transfer Learning

Natasha Shaukat et al.

Summary: This article presents a learning-based technique for the segmentation and classification of diabetic retinopathy (DR) lesions. Deep feature extraction and semantic segmentation are performed using pre-trained models, and a multi-classification model is used for feature extraction and classification. The proposed method achieves better results compared to the latest published works on multiple datasets.

JOURNAL OF PERSONALIZED MEDICINE (2022)

Proceedings Paper Computer Science, Artificial Intelligence

Diagnosis of Early-Stage Diabetic Retinopathy in Digital Fundus Images

D. Lavanya et al.

Summary: This article presents an automatic screening system for the early detection of diabetic retinopathy microaneurysms. The system utilizes image pre-processing, feature extraction, and classification methods to achieve improved outcomes.

2022 SECOND INTERNATIONAL CONFERENCE ON ADVANCES IN ELECTRICAL, COMPUTING, COMMUNICATION AND SUSTAINABLE TECHNOLOGIES (ICAECT) (2022)

Article Ophthalmology

Mathematical morphology for microaneurysm detection in fundus images

Shilpa Joshi et al.

EUROPEAN JOURNAL OF OPHTHALMOLOGY (2020)

Article Computer Science, Artificial Intelligence

A deep learning interpretable classifier for diabetic retinopathy disease grading

Jordi de la Torre et al.

NEUROCOMPUTING (2020)

Article Biology

Diabetic retinopathy detection using red lesion localization and convolutional neural networks

Gabriel Tozatto Zago et al.

COMPUTERS IN BIOLOGY AND MEDICINE (2020)

Article Computer Science, Artificial Intelligence

Geographic variation and ethnicity in diabetic retinopathy detection via deep learning

Ali Serener et al.

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES (2020)

Article Physics, Multidisciplinary

Entropy Rate Superpixel Classification for Automatic Red Lesion Detection in Fundus Images

Roberto Romero-Oraa et al.

ENTROPY (2019)

Article Computer Science, Artificial Intelligence

L-Seg: An end-to-end unified framework for multi-lesion segmentation of fundus images

Song Guo et al.

NEUROCOMPUTING (2019)

Article Computer Science, Hardware & Architecture

Automatic Diabetic Retinopathy Screening via Cascaded Framework Based on Image- and Lesion-Level Features Fusion

Cheng-Zhang Zhu et al.

JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY (2019)

Proceedings Paper Automation & Control Systems

Red lesion detection in fundus images based on convolution neural network

Xiao-nan Fan et al.

PROCEEDINGS OF THE 2019 31ST CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2019) (2019)

Article Computer Science, Interdisciplinary Applications

An ensemble deep learning based approach for red lesion detection in fundus images

Jose Ignacio Orlando et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2018)

Article Computer Science, Interdisciplinary Applications

Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening

Lama Seoud et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2015)

Article Materials Science, Multidisciplinary

FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE

Etienne Decenciere et al.

IMAGE ANALYSIS & STEREOLOGY (2014)

Article Computer Science, Information Systems

DREAM: Diabetic Retinopathy Analysis Using Machine Learning

Sohini Roychowdhury et al.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2014)

Article Engineering, Biomedical

An Ensemble-Based System for Microaneurysm Detection and Diabetic Retinopathy Grading

Balint Antal et al.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2012)

Article Computer Science, Information Systems

A Modified Matched Filter With Double-Sided Thresholding for Screening Proliferative Diabetic Retinopathy

Lei Zhang et al.

IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE (2009)