부산대학교 도서관

In recent years, convolutional neural networks（CNNs）have been exploited 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 histologically classifying a dataset of 548 lung CT images into 5 categories, e.g. lung adenocarcinoma, lung squamous cell carcinoma, metastatic lung cancer, potential lung cancer and normal. The main difference between the commonly-used CNNs and the presented method is that we use redundant wavelet coefficients at level 1 as inputs to the CNN instead of using original images. One of the major advantages of the proposed method is that it is no need to extract the regions of interest from images in advance. 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 can achieve the highest overall accuracy of 91.7% and demonstrate the potential for use in classification of lung diseases in CT images.