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AOCT-NET: a convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images.

Since introducing optical coherence tomography (OCT) technology for 2D eye imaging, it has become one of the most important and widely used imaging modalities for the noninvasive assessment of retinal eye diseases. Age-related macular degeneration (AMD) and diabetic macular edema eye disease are the...

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Published in:Medical & Biological Engineering & Computing Vol. 58; no. 1; pp. 41 - 54
Main Author: Alqudah, Ali Mohammad
Format: Journal Article
Published: Springer Nature Jan2020
Online Access:View this record in EBSCOhost
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      dt: Jan2020
      vid: 58
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      pub: Springer Nature
      place: New York, New York
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        atl: AOCT-NET: a convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images.
      aug:
        au: Alqudah, Ali Mohammad
        affil: Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid, Jordan
      sug:
        subj:
          Retinal Diseases
          Tomography, Optical Coherence
          Algorithms
          Retinal Diseases Classification
          Imaging, Three-Dimensional
          ROC Curve
          Physics
          Automation
          Databases
          User-Computer Interface
      ab: Since introducing optical coherence tomography (OCT) technology for 2D eye imaging, it has become one of the most important and widely used imaging modalities for the noninvasive assessment of retinal eye diseases. Age-related macular degeneration (AMD) and diabetic macular edema eye disease are the leading causes of blindness being diagnosed using OCT. Recently, by developing machine learning and deep learning techniques, the classification of eye retina diseases using OCT images has become quite a challenge. In this paper, a novel automated convolutional neural network (CNN) architecture for a multiclass classification system based on spectral-domain optical coherence tomography (SD-OCT) has been proposed. The system used to classify five types of retinal diseases (age-related macular degeneration (AMD), choroidal neovascularization (CNV), diabetic macular edema (DME), and drusen) in addition to normal cases. The proposed CNN architecture with a softmax classifier overall correctly identified 100% of cases with AMD, 98.86% of cases with CNV, 99.17% cases with DME, 98.97% cases with drusen, and 99.15% cases of normal with an overall accuracy of 95.30%. This architecture is a potentially impactful tool for the diagnosis of retinal diseases using SD-OCT images.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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