北京生物醫(yī)學工程

基于改進YOLO算法的肺部CT圖像中結節(jié)檢測研究

Study on nodule detection in lung CT images based on improved YOLO algorithm

作者：王波馮旭鵬劉利軍黃青松

單位：昆明理工大學信息工程與自動化學院(昆明 650500) 云南省計算機技術應用重點實驗室(昆明650500) 昆明理工大學教育技術與網(wǎng)絡中心(昆明650500）

關鍵詞： YOLO算法; CT圖像; 肺結節(jié)檢測; 多尺度預測; 目標識別

分類號：R318.04

出版年·卷·期（頁碼）：2020·39·6（615-621）

摘要：

目的隨著機器學習的發(fā)展，如何準確高效地識別肺部CT圖像中的肺結節(jié)具有重要的應用價值。方法針對肺部結構復雜、肺部結節(jié)過小、肺結節(jié)病理特征各異等特點。提出一個以YOLO算法為基礎，結合Darknet-53網(wǎng)絡和Densenet網(wǎng)絡的思想，在多尺度間具有緊密連接的深度卷積神經(jīng)網(wǎng)絡。為保證圖像有效信息和提高目標定位的精確性以及檢測的召回率，首先對數(shù)據(jù)集圖像尺寸大小進行固定，其次通過使用K-means算法對數(shù)據(jù)集進行聚類分析。最后將使用二元交叉熵做類別預測。實驗使用美國癌癥研究所公開的肺部圖像數(shù)據(jù)集聯(lián)盟（lung image databse consortium, LIDC）提供的數(shù)據(jù)集，對肺結節(jié)檢測的準確率以及檢測效率進行了實驗對比。結果改進的深度卷積神經(jīng)網(wǎng)絡對肺結節(jié)檢測的準確率及檢測效率均有提升。在肺部CT圖像中肺結節(jié)檢測的平均查全率達到95.69%，對微小結節(jié)的平均查全率達到88.66%，每秒識別幀數(shù)達到32 f/s，相比當前最快的Faster R-CNN檢測時間縮短了近80%。結論通過對YOLO算法的改進可以提高肺結節(jié)檢測效率，為肺部CT圖像肺結節(jié)實時檢測提供了條件。

Objective With the development of machine learning, how to accurately and efficiently identify pulmonary nodules in CT images of the lung has important application value. Methods The pulmonary structure is complex, the pulmonary nodules are too small, and the pulmonary nodules have different pathological characteristics. Based on YOLO algorithm, a deep convolutional neural network with close connection between multiple scales is proposed, which combines the darknet-53 network and Densenet network. In order to ensure the effective information of the image and improve the accuracy of the target positioning and the recall rate of detection, the image size of the data set is fixed firstly, and then the clustering analysis of the data set is carried out by using k-means algorithm.Finally, binary cross entropy will be used to make category prediction. The study used data sets from the Lung Image database Consortium (LIDC), which is publicly available from the national cancer institute, to compare the accuracy and efficiency of Lung nodule detection. Results The improved deep convolutional neural network improves the accuracy and efficiency of lung nodule detection.In the lung CT images, the average recall rate of lung nodules detection was 95.69%, the average recall rate of tiny nodules was 88.66%, and the recognition frames per second reached 32f/s, which was nearly 80% shorter than the current fastest detection time of Faster r-cnn. Conclusions The improvement of YOLO algorithm can improve the detection efficiency of pulmonary nodules and provide conditions for real-time detection of pulmonary nodules in CT images.

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