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Deep convolutional neural network-based patch classification for retinal nerve fiber layer defect detection in early

Rashmi Panda1, Niladri B Puhan1, Aparna Rao2

  • 1IIT Bhubaneswar, School of Electrical Sciences, Bhubaneswar, India.

Journal of Medical Imaging (Bellingham, Wash.)
|March 7, 2019
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Summary
This summary is machine-generated.

Early detection of glaucoma is crucial for preventing blindness. This study introduces a cost-effective deep learning method using redfree fundus images to accurately identify retinal nerve fiber layer defects, aiding in early glaucoma risk assessment.

Keywords:
convolution neural networkdeep learningfundus imageglaucomapatch classificationretinal nerve fiber layer

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Glaucoma is a progressive optic neuropathy leading to irreversible blindness.
  • Early detection of retinal nerve fiber layer defects (RNFLD) is key for timely intervention.
  • Visual detection of RNFLDs is challenging due to low contrast.

Purpose of the Study:

  • To develop a cost-effective method for RNFLD detection using redfree fundus imaging.
  • To implement a computer-assisted approach for early glaucoma risk assessment.
  • To accurately localize RNFLD boundaries for improved diagnostic capabilities.

Main Methods:

  • A deep convolutional neural network (CNN) based patch classification strategy was formulated.
  • The CNN model was trained on a large dataset of RNFLD and background image patches.
  • Redfree fundus imaging was utilized for its cost-effectiveness and suitability for RNFLD visualization.

Main Results:

  • The proposed CNN approach achieved accurate RNFLD boundary pixel classification.
  • Enhanced RNFLD detection performance was observed.
  • The model demonstrated a sensitivity of 0.8205 and a false positive rate of 0.2000 per image.

Conclusions:

  • Deep CNNs offer a promising solution for automated RNFLD detection.
  • Redfree fundus imaging combined with AI can facilitate early glaucoma risk assessment.
  • The developed method shows potential for practical clinical application in early glaucoma diagnosis.