Author(s)

Eri Matsuyama¹, Du-Yih Tsai²

¹Department of Radiological Sciences, International University of Health and Welfare, ōtawara, Japan.
²Department of Biomedical Engineering, Hungkuang University, Taichung.

Recently, convolutional neural networks (CNNs) have been utilized in medical imaging research field and have successfully shown their ability in image classification and detection. In this paper we used a CNN combined with a wavelet transform approach for classifying a dataset of 448 lung CT images into 4 categories, e.g. lung adenocarcinoma, lung squamous cell carcinoma, metastatic lung cancer, and normal. The key difference between the commonly-used CNNs and the presented method is that in this method, we adopt the use of redundant wavelet coefficients at level 1 as inputs to the CNN, instead of using original images. One of the main advantages of the proposed method is that it is not necessary to extract regions of interest from original images. The wavelet coefficients of the entire image are used as inputs to the CNN. We compare the classification performance of the proposed method to that of an existing CNN classifier and a CNN-based support vector machine classifier. The experimental results show that the proposed method outperforms the other two methods and achieve the highest overall accuracy of 91.9%. It demonstrates the potential for use in classification of lung diseases in CT images.

Convolutional Neural Networks, Wavelet Transforms, Classification, Lung Diseases, CT Imaging

Matsuyama, E. and Tsai, D. (2018) Automated Classification of Lung Diseases in Computed Tomography Images Using a Wavelet Based Convolutional Neural Network. Journal of Biomedical Science and Engineering, 11, 263-274. doi: 10.4236/jbise.2018.1110022.