Posteroanterior Chest X-ray Image Classification with a Multilayer 1D Convolutional Neural Network-Based Classifier for Cardiomegaly Level Screening

Chia Hung Lin, Feng Zhou Zhang, Jian Xing Wu, Ning Sheng Pai, Pi Yun Chen, Ching Chou Pai, Chung Dann Kan

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

Palpitations, chest tightness, and shortness of breath are early indications of cardiomegaly, which is an asymptomatic disease. Their causes and treatment strategies are different due to differing indications. Hence, early screening of cardiomegaly levels can be used to make a strategy for administering drugs and surgical treatments. In this study, we will establish a multilayer one-dimensional (1D) convolutional neural network (CNN)-based classifier for automatic cardiomegaly level screening based on chest X-ray (CXR) image classification in frontal posteroanterior view. Using two-round 1D convolutional processes in the convolutional pooling layer, two-dimensional (2D) feature maps can be converted into feature signals, which can enhance their characteristics for identifying normal condition and cardiomegaly levels. In the classification layer, a classifier based on gray relational analysis, which has a straightforward mathematical operation, is used to screen the cardiomegaly levels. Based on the collected datasets from the National Institutes of Health CXR image database, the proposed multilayer 1D CNN-based classifier with K-fold cross-validation has promising results for the intended medical purpose, with precision of 97.80%, recall of 98.20%, accuracy of 98.00%, and F1 score of 0.9799.

原文English
文章編號1364
期刊Electronics (Switzerland)
11
發行號9
DOIs
出版狀態Published - 2022 5月 1

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 訊號處理
  • 硬體和架構
  • 電腦網路與通信
  • 電氣與電子工程

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