北京生物醫(yī)學工程

基于深度遷移學習的老年人群眼底疾病輔助診斷研究

Fundus diseases of the elderly detection algorithm based on deep transfer learning

作者：楊康趙太宏

單位：南京醫(yī)科大學公共衛(wèi)生學院（南京 211112）;<br />南京醫(yī)科大學附屬南京醫(yī)院院長辦公室（南京 210006）<br />通信作者：趙太宏。E-mail:[email protected]

分類號： R318.04 

出版年·卷·期（頁碼）：2022·41·5（465-470）

摘要：

目的提出一種基于深度遷移學習的老年人群眼底疾病自動分類方法，為老年人眼底疾病提供輔助診斷分析。方法以2 048張眼底圖像作為原始眼底數據集。首先，對眼底圖像進行去除模糊不清、圖像縮放和對比度調整等預處理操作。然后隨機將眼底圖像劃分成訓練組、驗證組和測試組。遷移卷積神經網絡（convolution neural network，CNN）在其他分類任務預訓練的參數，然后根據眼底病分類任務微調CNN模型全連接層參數，經過網絡優(yōu)化最終搭建眼底病分類診斷網絡模型。最后使用獨立的測試集對網絡模型進行測試，并通過混淆矩陣查看模型的分類情況。最終，以受試者工作特征曲線下面積（area under curve，AUC）、準確率、靈敏度、特異度和F1值等指標評價網絡模型對眼底病的分類診斷效果。結果 CNN模型在眼底病分類準確率均高于0.90，靈敏度在0.90~0.95范圍，特異度在0.89~0.95范圍，AUC在0.87~0.92范圍，F1值在0.92~0.94范圍。結論提出基于遷移學習方法訓練的CNN模型以較高的準確率實現老年人眼底病的自動分類診斷，且具備訓練周期短和訓練參數少等優(yōu)勢。該方法將有助于提高基層社區(qū)對老年人群眼底疾病的輔助診斷能力。

Objective To propose an automatic classification method of fundus diseases for the elderly based on deep transfer learning, and to provide auxiliary diagnosis and analysis of fundus diseases for the elderly. Methods Total of 2048 fundus images were used as the original fundus dataset. First, the fundus image is preprocessed, such as removing blur, image scaling and contrast adjustment.Then,The fundus images were randomly divided into training group, validation group and testing group. The parameters of the convolutional neural network (CNN) pre-trained in other classification tasks were transferred, and then the parameters of the fully connected layer of the CNN model were fine-tuned according to the fundus disease classification task. After network optimization, the fundus disease classification and diagnosis network model was finally built. Finally, an independent test set was used to test the network model, and the classification of the model was checked through the confusion matrix. At last, the classification and diagnosis effect of the network model on fundus diseases was evaluated by indicators such as area under curve (AUC), accuracy, sensitivity, specificity and F1 score. Results The classification accuracy of CNN model in fundus diseases was higher than 0.90, the sensitivity was in the range of 0.90-0.95, the specificity was in the range of 0.89-0.95, the AUC was in the range of 0.87-0.92, and the F1 score was in the range of 0.92-0.94. Conclusions The CNN model trained based on the transfer learning method is proposed to achieve automatic classification and diagnosis of fundus diseases in the elderly with high accuracy, and has the advantages of short training period and few training parameters. This method will help to improve the auxiliary diagnosis ability of the elderly population for fundus diseases in the grass-roots community.

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