A Novel Lung Cancer Detection Method Using Wavelet Decomposition and Convolutional Neural Network  

作　　者：Ahmad M. Sarhan 

机构地区：[1]Department of Computer Engineering, Amman Arab University, Amman, Jordan

出　　处：《Journal of Biomedical Science and Engineering》2020年第5期81-92,共12页生物医学工程（英文）

摘　　要：Computerized tomography (CT) scan is the only screening test recommended by doctors to look for lung cancer. Convolutional neural networks (CNNs) have recently proven their ability to successfully classify medical images. Due to its strong compactness property, the Discrete Wavelet transform (DWT) has been commonly used in image feature extraction applications. This paper presents a novel technique for the classification of Lung cancer in Computerized Tomography (CT) scans using Wavelets to find discriminative features in the CT images and CNN to classify the extracted features. Experimental results prove that the proposed approach outperforms other commonly used methods and gives an overall accuracy of 99.5%.Computerized tomography (CT) scan is the only screening test recommended by doctors to look for lung cancer. Convolutional neural networks (CNNs) have recently proven their ability to successfully classify medical images. Due to its strong compactness property, the Discrete Wavelet transform (DWT) has been commonly used in image feature extraction applications. This paper presents a novel technique for the classification of Lung cancer in Computerized Tomography (CT) scans using Wavelets to find discriminative features in the CT images and CNN to classify the extracted features. Experimental results prove that the proposed approach outperforms other commonly used methods and gives an overall accuracy of 99.5%.

关 键 词：Convolutional Neural Network CNN) WAVELET TRANSFORM Image Classification LUNG Cancer COMPUTERIZED TOMOGRAPHY (CT) 

分 类 号：O17[理学—数学]