Attention Mechanism-Based Glaucoma Classification Model Using Retinal Fundus Images

被引:1
|
作者
Cho, You-Sang [1 ]
Song, Ho-Jung [1 ]
Han, Ju-Hyuck [1 ]
Kim, Yong-Suk [2 ]
机构
[1] Konyang Univ, Dept Biomed Engn, Daejeon 35365, South Korea
[2] Konyang Univ, Dept Artificial Intelligence, Daejeon 35365, South Korea
关键词
attention; classification; causality; artificial intelligence;
D O I
10.3390/s24144684
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
This paper presents a classification model for eye diseases utilizing attention mechanisms to learn features from fundus images and structures. The study focuses on diagnosing glaucoma by extracting retinal vessels and the optic disc from fundus images using a ResU-Net-based segmentation model and Hough Circle Transform, respectively. The extracted structures and preprocessed images were inputted into a CNN-based multi-input model for training. Comparative evaluations demonstrated that our model outperformed other research models in classifying glaucoma, even with a smaller dataset. Ablation studies confirmed that using attention mechanisms to learn fundus structures significantly enhanced performance. The study also highlighted the challenges in normal case classification due to potential feature degradation during structure extraction. Future research will focus on incorporating additional fundus structures such as the macula, refining extraction algorithms, and expanding the types of classified eye diseases.
引用
收藏
页数:9
相关论文
共 50 条
  • [1] Glaucoma Detection with Retinal Fundus Images Using Segmentation and Classification
    Shyamalee, Thisara
    Meedeniya, Dulani
    MACHINE INTELLIGENCE RESEARCH, 2022, 19 (06) : 563 - 580
  • [2] Glaucoma Detection with Retinal Fundus Images Using Segmentation and Classification
    Thisara Shyamalee
    Dulani Meedeniya
    Machine Intelligence Research, 2022, 19 : 563 - 580
  • [3] Glaucoma Detection with Retinal Fundus Images Using Segmentation and Classification
    Thisara Shyamalee
    Dulani Meedeniya
    Machine Intelligence Research, 2022, 19 (06) : 563 - 580
  • [4] Analysis of Retinal Fundus Images for Classification of Glaucoma
    Muthmainah, Maria Ulfa
    Nugroho, Hanung Adi
    Winduratna, Bondhan
    Ilcham
    2018 1ST INTERNATIONAL CONFERENCE ON BIOINFORMATICS, BIOTECHNOLOGY, AND BIOMEDICAL ENGINEERING - BIOINFORMATICS AND BIOMEDICAL ENGINEERING, 2018, : 7 - 12
  • [5] Detection of Glaucoma Using Retinal Fundus Images
    Khan, Fauzia
    Khan, Shoaib A.
    Yasin, Ubaid Ullah
    ul Haq, Ihtisham
    Qamar, Usman
    6TH BIOMEDICAL ENGINEERING INTERNATIONAL CONFERENCE (BMEICON 2013), 2013,
  • [6] Detection of Glaucoma Using Retinal Fundus Images
    Ahmad, Hafsah
    Yamin, Abubakar
    Shakeel, Aqsa
    Gillani, Syed Omer
    Ansari, Umer
    2014 INTERNATIONAL CONFERENCE ON ROBOTICS AND EMERGING ALLIED TECHNOLOGIES IN ENGINEERING (ICREATE), 2014, : 321 - 324
  • [7] Diabetic Retinopathy Classification Based on Segmented Retinal Vasculature of Fundus Images Using Attention U-NET
    Fousiya, T. T.
    Ahammed, Muneer K., V
    2022 IEEE 19TH INDIA COUNCIL INTERNATIONAL CONFERENCE, INDICON, 2022,
  • [8] Detection of glaucoma using retinal fundus images: A comprehensive review
    Shabbir, Amsa
    Rasheed, Aqsa
    Shehraz, Huma
    Saleem, Aliya
    Zafar, Bushra
    Sajid, Muhammad
    Ali, Nouman
    Dar, Saadat Hanif
    Shehryar, Tehmina
    MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2021, 18 (03) : 2033 - 2076
  • [9] CA-Net: A Novel Cascaded Attention-Based Network for Multistage Glaucoma Classification Using Fundus Images
    Das, Dipankar
    Nayak, Deepak Ranjan
    Pachori, Ram Bilas
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2023, 72 : 1 - 10
  • [10] Automated detection of glaucoma from retinal fundus images using a variety of fundus cameras
    Gunasinghe, Hansi N.
    McKelvie, James
    Koay, Abigail
    Mayo, Michael
    CLINICAL AND EXPERIMENTAL OPHTHALMOLOGY, 2022, 49 (08): : 911 - 911