Intelligent classification of lung malignancies using deep learning techniques

作     者：Priyanka Yadlapalli D.Bhavana Suryanarayana Gunnam 

作者机构：Department of Electronics and Communication EngineeringKoneru Lakshmaiah Education FoundationGunturIndia Department of Electronics and CommunicationsGokaraju Rangaraju Institute of Engineering and TechnologyHyderabadIndia Department of Electronics and CommunicationsVRSiddhartha Engineering CollegeVijayawadaIndia Shanghai Jiao Tong University-Minhang CampusShanghaiChina 

出 版 物：《International Journal of Intelligent Computing and Cybernetics》 (智能计算与控制论国际期刊（英文）)

年 卷 期：2022年第15卷第3期

页      面：345-362页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0710[理学-生物学] 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] 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] 10[医学] 

主　　题：Computed tomography scans Convolution neural networks Transfer learning Intelligent classification State-of-the-art(SOTA)accuracy 

摘      要：Purpose-Computed tomography(CT)scan can provide valuable information in the diagnosis of lung *** detect the location of the cancerous lung nodules,this work uses novel deep learning *** majority of the early investigations used CT,magnetic resonance and mammography *** appropriate procedures,the professional doctor in this sector analyses these images to discover and diagnose the various degrees of lung *** of the methods used to discover and detect cancer illnesses are time-consuming,expensive and stressful for the *** address all of these issues,appropriate deep learning approaches for analyzing these medical images,which included CT scan images,were ***/methodology/approach-Radiologists currently employ chest CT scans to detect lung cancer at an early *** certain situations,radiologists’perception plays a critical role in identifying lung melanoma which is incorrectly *** learning is a new,capable and influential approach for predicting medical *** this paper,the authors employed deep transfer learning algorithms for intelligent classification of lung *** neural networks(VGG16,VGG19,MobileNet and DenseNet169)are used to constrain the input and output layers of a chest CT scan image ***-The collection includes normal chest CT scan pictures as well as images from two kinds of lung cancer,squamous and adenocarcinoma impacted chest CT scan *** to the confusion matrix results,the VGG16 transfer learning technique has the highest accuracy in lung cancer classification with 91.28% accuracy,followed by VGG19 with 89.39%,MobileNet with 85.60% and DenseNet169 with 83.71% accuracy,which is analyzed using Google ***/value-The proposed approach using VGG16 maximizes the classification accuracy when compared to VGG19,MobileNet and *** results are validated by computing the confusion matrix for each network type.