Auxiliary Classifier of Generative Adversarial Network for Lung Cancer Diagnosis  

作　　者：P.S.Ramapraba P.Epsiba K.Umapathy E.Sivanantham 

机构地区：[1]Department of Electrical and Electronics Engineering,Panimalar Institute of Technology,Chennai,Tamil Nadu,India [2]Department of Electronics and Communication Engineering,Sri Indu College of Engineering and Technology,Hyderabad,India [3]Department of Electronics and Communication Engineering,Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya University,(Deemed University),Kanchipuram,Tamil Nadu,India [4]Electronics and Communication Engineering,Saveetha School of Engineering,Saveetha Institute of Medical and Technical Sciences,Chennai,Tamil Nadu,India

出　　处：《Intelligent Automation & Soft Computing》2023年第5期2177-2189,共13页智能自动化与软计算（英文）

摘　　要：The classification of lung nodules is a challenging problem as the visual analysis of the nodules and non-nodules revealed homogenous textural patterns.In this work,an Auxiliary Classifier(AC)-Generative Adversarial Network(GAN)based Lung Cancer Classification(LCC)system is developed.The pro-posed AC-GAN-LCC system consists of three modules;preprocessing,Lungs Region Detection(LRD),and AC-GAN classification.A Wienerfilter is employed in the preprocessing module to remove the Gaussian noise.In the LRD module,only the lung regions(left and right lungs)are detected using itera-tive thresholding and morphological operations.In order to extract the lung region only,floodfilling and background subtraction.The detected lung regions are fed to the AC-GAN classifier to detect the nodules.It classifies the nodules into one of the two classes,i.e.,binary classification(such as nodules or non-nodules).The AC-GAN is the extended version of the conditional GAN that predicts the label of a given image.Three different optimization techniques,adaptive gradient optimi-zation,root mean square propagation optimization,and Adam optimization are employed for optimizing the AC-GAN architecture.The proposed AC-GAN-LCC system is evaluated on the Lung Image Database Consortium(LIDC)data-base Computed Tomography(CT)scan images.The proposed AC-GAN-LCC system classifies∼15000 CT slices(7310 non-nodules and 7685 nodules).It pro-vides an overall accuracy of 98.8%on the LIDC database using Adam optimiza-tion by a 10-fold cross-validation approach.

关 键 词：Lung cancer generative adversarial network auxiliary classifier image classification system deep learning 

分 类 号：R730.4[医药卫生—肿瘤]