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FN-OCT: Disease Detection Algorithm for Retinal Optical Coherence Tomography Based on a Fusion Network by Zhuang Ai; Xuan Huang; Jing Feng; Hui Wang; Yong Tao; Fanxin Zeng; Yaping Lu is a Neuroscience article available to read on EtoBox.

What is FN-OCT: Disease Detection Algorithm for Retinal Optical Coherence Tomography Based on a Fusion Network about?

Optical coherence tomography (OCT) is a new type of tomography that has experienced rapid development and potential in recent years. It is playing an increasingly important role in retinopathy diagnoses. At present, due to the uneven distributions of medical resources in various regions, the uneven proficiency levels of doctors in grassroots and remote areas, and the development needs of rare disease diagnosis and precision medicine, artificial intelligence technology based on deep learning can provide fast, accurate, and effective solutions for the recognition and diagnosis of retinal OCT images. To prevent vision damage and blindness caused by the delayed discovery of retinopathy, a fusion network (FN)-based retinal OCT classification algorithm (FN-OCT) is proposed in this paper to improve upon the adaptability and accuracy of traditional classification algorithms. The InceptionV3, Inception-ResNet, and Xception deep learning algorithms are used as base classifiers, a convolutional block attention mechanism (CBAM) is added after each base classifier, and three different fusion strategies are used to merge the prediction results of the base classifiers to output the final predicti

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Author

Zhuang Ai; Xuan Huang; Jing Feng; Hui Wang; Yong Tao; Fanxin Zeng; Yaping Lu

Publisher

Frontiers Media SA

Published

2022

Language

EN

Field

Neuroscience (Life Sciences)