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

Abstract

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 prediction results (choroidal neovascularization (CNV), diabetic macular oedema (DME), drusen, normal). The results show that in a classification problem involving the UCSD common retinal OCT dataset (108,312 OCT images from 4,686 patients), compared with that of the InceptionV3 network model, the prediction accuracy of FN-OCT is improved by 5.3% (accuracy = 98.7%, area under the curve (AUC) = 99.1%). The predictive accuracy and AUC achieved on an external dataset for the classification of retinal OCT diseases are 92 and 94.5%, respectively, and gradient-weighted class activation mapping (Grad-CAM) is used as a visualization tool to verify the effectiveness of the proposed FNs. This finding indicates that the developed fusion algorithm can significantly improve the performance of classifiers while providing a powerful tool and theoretical support for assisting with the diagnosis of retinal OCT.

Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy

“…For OCT images, we have also applied a fusion network algorithm to the retinal lesion classification of choroidal neovascularization (CNV), diabetic macular edema (DME), drusen, and normal groups. The result showed that the developed fusion algorithm can significantly improve the performance of classifiers compared to traditional algorithms while providing a powerful tool and theoretical support to assist with the diagnosis of retinal OCT images ( 16 ).…”

New Advances in Artificial Intelligence for Biomedical Research and Clinical Decision-Making

“…By detecting patterns and anomalies in the data that human doctors may miss, AI can inform clinical decision-making and suggest potential treatments. Additionally, AI can predict the likelihood of a patient developing a particular disease or condition, allowing doctors to take preventive measures to reduce the risk of future complications [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”

“…It can segment and detect vasculature (Fig. 2.A), shadowing artifacts, perfused areas, and even diagnose the severity of the disease state [21][22][23]. In biomedical engineering, AI is also being used to enhance the performance of optical instruments and instruments used in research.…”

On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images

“…Zhuang Ai et al [ 66 ] proposed a fusion network (FN)-based algorithm for the classification of retinal optical coherence tomography (OCT) images. The FN-OCT algorithm combines the InceptionV3, Inception-ResNet, and Xception deep learning algorithms with a convolutional block attention mechanism and three different fusion strategies to improve the adaptability and accuracy of traditional classification algorithms.…”