Cover Image
QR Code

Diabetic Retinopathy Prediction Based on Wavelet Decomposition and Modified Capsule Network.

Diabetic retinopathy (DR) is one of the most common consequences of diabetes. It affects the retina, causing blood vessel damage which can lead to loss of vision. Saving patients from losing their sight or at least slowing the progress of this disease depends mainly on the early detection of this pa...

Full description

Published in:Journal of Digital Imaging Vol. 36; no. 4; pp. 1739 - 1752
Main Authors: Oulhadj, Mohammed, Riffi, Jamal, Khodriss, Chaimae, Mahraz, Adnane Mohamed, Bennis, Ahmed, Yahyaouy, Ali, Chraibi, Fouad, Abdellaoui, Meriem, Andaloussi, Idriss Benatiya, Tairi, Hamid
Format: equations & formulas pictorial research tables/charts Journal Article
Published: Springer Nature Aug2023
Online Access:View this record in EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=169808808&site=ehost-live
header:
  @attributes:
    shortDbName: ccm
    uiTerm: 169808808
    longDbName: CINAHL Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    dissinfo:
    jinfo:
      jid:
        08971889
        DOQ
      jtl: Journal of Digital Imaging
      issn: 08971889
      maglogo: N
    pubinfo:
      dt: Aug2023
      vid: 36
      iid: 4
      pid: 237
      pub: Springer Nature
      place: New York, New York
    artinfo:
      ui:
        169808808
        162702176
        169808808
        169808808
        10.1007/s10278-023-00813-0
        169808808
      ppf: 1739
      ppct: 13
      formats:
        fmt:
          – @attributes:
              type: T
          – @attributes:
              type: P
      tig:
        atl: Diabetic Retinopathy Prediction Based on Wavelet Decomposition and Modified Capsule Network.
      aug:
        au:
          Oulhadj, Mohammed
          Riffi, Jamal
          Khodriss, Chaimae
          Mahraz, Adnane Mohamed
          Bennis, Ahmed
          Yahyaouy, Ali
          Chraibi, Fouad
          Abdellaoui, Meriem
          Andaloussi, Idriss Benatiya
          Tairi, Hamid
        affil: LISAC Laboratory, Department of Informatics, Universite Sidi Mohamed Ben Abdellah Faculte des Sciences Dhar El Mahraz, Fez, Morocco
      sug:
        subj:
          Diabetic Retinopathy Diagnosis
          Severity of Illness Evaluation
          Neural Networks (Computer)
          Signal Processing, Computer Assisted
          Human
          Deep Learning
          Algorithms
      ab: Diabetic retinopathy (DR) is one of the most common consequences of diabetes. It affects the retina, causing blood vessel damage which can lead to loss of vision. Saving patients from losing their sight or at least slowing the progress of this disease depends mainly on the early detection of this pathology, on top of the detection of its specific stage. Furthermore, the early detection of diabetic retinopathy and the follow-up of the patient's condition remains an arduous task, whether for an experienced expert ophthalmologist or a computer-aided diagnosis technician. In this paper, we aim to propose a new automatic diabetic retinopathy severity level detection method. The proposed approach merges the pyramid hierarchy of the discrete wavelet transform of the retina fundus image with the modified capsule network and the modified inception block proposed, in addition to a new deep hybrid model that concatenates the inception block with capsule networks. The performance of our proposed approach has been validated on the APTOS dataset, as it achieved a high training accuracy of 97.71% and a high testing accuracy score of 86.54%, which is considered one of the best scores achieved in this field using the same dataset.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
    refInfo:
    holdings:
      @attributes:
        islocal: N