Detection of Lung Nodules on X-ray Using Transfer Learning and Manual Features

作     者：Imran Arshad Choudhry Adnan N.Qureshi 

作者机构：Faculty of Information TechnologyUniversity of Central PunjabLahorePakistan 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第72卷第7期

页      面：1445-1463页

核心收录：

学科分类：0710[理学-生物学] 0831[工学-生物医学工程（可授工学、理学、医学学位）] 0711[理学-系统科学] 1002[医学-临床医学] 0805[工学-材料科学与工程（可授工学、理学学位）] 100214[医学-肿瘤学] 0701[理学-数学] 10[医学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0702[理学-物理学] 

主　　题：Lungs cancer convolutional neural network hand-crafted feature extraction deep learning classification 

摘      要：The well-established mortality rates due to lung cancers,scarcity of radiology experts and inter-observer variability underpin the dire need for robust and accurate computer aided diagnostics to provide a second opinion.To this end,we propose a feature grafting approach to classify lung cancer images from publicly available National Institute of Health(NIH)chest X-Ray dataset comprised of 30,805 unique patients.The performance of transfer learning with pre-trained VGG and Inception models is evaluated in comparison against manually extracted radiomics features added to convolutional neural network using custom layer.For classification with both approaches,Support VectorsMachines(SVM)are used.The results from the 5-fold cross validation report Area Under Curve(AUC)of 0.92 and accuracy of 96.87%in detecting lung nodules with the proposed method.This is a plausible improvement against the observed accuracy of transfer learning using Inception(79.87%).The specificity of allmethods is99%,however,the sensitivity of the proposed method(97.24%)surpasses that of transfer learning approaches(67%).Furthermore,it is observed that the true positive rate with SVM is highest at the same false-positive rate in experiments amongst Random Forests,Decision Trees,and K-Nearest Neighbor classifiers.Hence,the proposed approach can be used in clinical and research environments to provide second opinions very close to the experts’intuition.