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FN-OCT: Disease Detection Algorithm for Retinal Optical Coherence Tomography Based on a Fusion Network.

Zhuang Ai1, Xuan Huang2,3, Jing Feng2

  • 1Department of Research and Development, Sinopharm Genomics Technology Co., Ltd., Jiangsu, China.

Frontiers in Neuroinformatics
|July 5, 2022
PubMed
Summary

A new fusion network (FN)-based algorithm, FN-OCT, enhances optical coherence tomography (OCT) image analysis for retinopathy diagnosis. This AI approach significantly improves diagnostic accuracy, aiding in early detection and prevention of vision loss.

Keywords:
attention mechanismfusion networkmodel interpretabilityoptical coherence tomographyretinal disease

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Area of Science:

  • Ophthalmology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Deep Learning for Diagnostic Tools

Background:

  • Optical coherence tomography (OCT) is crucial for retinopathy diagnosis, but resource disparities and varying expertise hinder timely detection.
  • Delayed diagnosis of retinopathy can lead to vision damage and blindness.
  • Artificial intelligence (AI) offers potential for fast, accurate recognition and diagnosis of retinal OCT images.

Purpose of the Study:

  • To develop an advanced AI algorithm for retinal OCT image classification to improve diagnostic accuracy and adaptability.
  • To address limitations of traditional classification methods in recognizing complex retinal conditions.
  • To provide a reliable tool for early detection of retinopathy, preventing vision loss.

Main Methods:

  • Proposed a fusion network (FN)-based retinal OCT classification algorithm (FN-OCT).
  • Utilized InceptionV3, Inception-ResNet, and Xception as base deep learning classifiers, enhanced with a convolutional block attention mechanism (CBAM).
  • Employed three fusion strategies to combine base classifier predictions for final output (choroidal neovascularization (CNV), diabetic macular oedema (DME), drusen, normal).

Main Results:

  • FN-OCT achieved a 5.3% improvement in prediction accuracy over InceptionV3 on the UCSD dataset (accuracy = 98.7%, AUC = 99.1%).
  • Achieved 92% accuracy and 94.5% AUC on an external dataset for retinal OCT disease classification.
  • Gradient-weighted class activation mapping (Grad-CAM) visualization confirmed the effectiveness of the fusion network.

Conclusions:

  • The developed FN-OCT algorithm significantly enhances the performance of deep learning classifiers for retinal OCT image analysis.
  • This fusion algorithm offers a powerful tool and theoretical support for assisting in the diagnosis of retinal diseases.
  • The findings support the potential of AI in improving accessibility and accuracy of retinopathy diagnosis, especially in underserved areas.