LungNet: A hybrid deep-CNN model for lung cancer diagnosis using CT and wearable sensor-based medical IoT data

被引:116
作者
Faruqui, Nuruzzaman [1 ]
Abu Yousuf, Mohammad [1 ]
Whaiduzzaman, Md [1 ,3 ]
Azad, A. K. M. [2 ]
Barros, Alistair [3 ]
Moni, Mohammad Ali [4 ]
机构
[1] Jahangirnagar Univ, Inst Informat Technol, Dhaka 1342, Bangladesh
[2] Swinburne Univ Technol Sydney, Fac Sci Engn & Technol, Sydney, NSW, Australia
[3] Queensland Univ Technol, 2 George St, Brisbane, Qld 4000, Australia
[4] Univ Queensland, Fac Hlth & Behav Sci, Sch Hlth & Rehabil Sci, St Lucia, Qld 4072, Australia
关键词
LungNet; Stage classification; Medical internet of things; Feature enhancement; CNN Architecture; Centralized server; CLASSIFICATION; PERFORMANCE; STAGE; SYMPTOMS; NODULES;
D O I
10.1016/j.compbiomed.2021.104961
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Lung cancer, also known as pulmonary cancer, is one of the deadliest cancers, but yet curable if detected at the early stage. At present, the ambiguous features of the lung cancer nodule make the computer-aided automatic diagnosis a challenging task. To alleviate this, we present LungNet, a novel hybrid deep-convolutional neural network-based model, trained with CT scan and wearable sensor-based medical IoT (MIoT) data. LungNet consists of a unique 22-layers Convolutional Neural Network (CNN), which combines latent features that are learned from CT scan images and MIoT data to enhance the diagnostic accuracy of the system. Operated from a centralized server, the network has been trained with a balanced dataset having 525,000 images that can classify lung cancer into five classes with high accuracy (96.81%) and low false positive rate (3.35%), outperforming similar CNN-based classifiers. Moreover, it classifies the stage-1 and stage-2 lung cancers into 1A, 1B, 2A and 2B sub-classes with 91.6% accuracy and false positive rate of 7.25%. High predictive capability accompanied with sub-stage classification renders LungNet as a promising prospect in developing CNN-based automatic lung cancer diagnosis systems.
引用
收藏
页数:16
相关论文
共 50 条
[1]   Genetic algorithm based adaptive histogram equalization (GAAHE) technique for medical image enhancement [J].
Acharya, Upendra Kumar ;
Kumar, Sandeep .
OPTIK, 2021, 230
[2]   Efficient Lung Nodule Classification Using Transferable Texture Convolutional Neural Network [J].
Ali, Imdad ;
Muzammil, Muhammad ;
Ul Haq, Ihsan ;
Khaliq, Amir A. ;
Abdullah, Suheel .
IEEE ACCESS, 2020, 8 :175859-175870
[3]   Special Section Guest Editorial: LUNGx Challenge for computerized lung nodule classification: Reflections and lessons learned [J].
Armato, Samuel G. ;
Hadjiiski, Lubomir ;
Tourassi, Georgia D. ;
Drukker, Karen ;
Giger, Maryellen L. ;
Li, Feng ;
Redmond, George ;
Farahani, Keyvan ;
Kirby, Justin S. ;
Clarke, Laurence P. .
Journal of Medical Imaging, 2015, 2 (02)
[4]   The Lung Image Database Consortium, (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans [J].
Armato, Samuel G., III ;
McLennan, Geoffrey ;
Bidaut, Luc ;
McNitt-Gray, Michael F. ;
Meyer, Charles R. ;
Reeves, Anthony P. ;
Zhao, Binsheng ;
Aberle, Denise R. ;
Henschke, Claudia I. ;
Hoffman, Eric A. ;
Kazerooni, Ella A. ;
MacMahon, Heber ;
van Beek, Edwin J. R. ;
Yankelevitz, David ;
Biancardi, Alberto M. ;
Bland, Peyton H. ;
Brown, Matthew S. ;
Engelmann, Roger M. ;
Laderach, Gary E. ;
Max, Daniel ;
Pais, Richard C. ;
Qing, David P-Y ;
Roberts, Rachael Y. ;
Smith, Amanda R. ;
Starkey, Adam ;
Batra, Poonam ;
Caligiuri, Philip ;
Farooqi, Ali ;
Gladish, Gregory W. ;
Jude, C. Matilda ;
Munden, Reginald F. ;
Petkovska, Iva ;
Quint, Leslie E. ;
Schwartz, Lawrence H. ;
Sundaram, Baskaran ;
Dodd, Lori E. ;
Fenimore, Charles ;
Gur, David ;
Petrick, Nicholas ;
Freymann, John ;
Kirby, Justin ;
Hughes, Brian ;
Casteele, Alessi Vande ;
Gupte, Sangeeta ;
Sallam, Maha ;
Heath, Michael D. ;
Kuhn, Michael H. ;
Dharaiya, Ekta ;
Burns, Richard ;
Fryd, David S. .
MEDICAL PHYSICS, 2011, 38 (02) :915-931
[5]  
Ashhar S. M., 2021, Int. J. Adv. Technol. Eng. Explor., V8, P126, DOI [DOI 10.19101/IJATEE.2020.S1762126, 10.]
[6]   CNN-based transfer learning-BiLSTM network: A novel approach for COVID-19 infection detection [J].
Aslan, Muhammet Fatih ;
Unlersen, Muhammed Fahri ;
Sabanci, Kadir ;
Durdu, Akif .
APPLIED SOFT COMPUTING, 2021, 98
[7]   Deep learning for lung Cancer detection and classification [J].
Asuntha, A. ;
Srinivasan, Andy .
MULTIMEDIA TOOLS AND APPLICATIONS, 2020, 79 (11-12) :7731-7762
[8]   Initial evaluation of the patient with lung cancer - Symptoms, signs, laboratory tests, and paraneoplastic syndromes [J].
Beckles, MA ;
Spiro, SG ;
Colice, GL ;
Rudd, RM .
CHEST, 2003, 123 (01) :97S-104S
[9]   Feature subset selection for improving the performance of false positive reduction in lung nodule CAD [J].
Boeroezky, Lilla ;
Zhao, Luyin ;
Lee, K. P. .
IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, 2006, 10 (03) :504-511
[10]   RS-DCNN: A novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification [J].
Boulila, Wadii ;
Sellami, Mokhtar ;
Driss, Maha ;
Al-Sarem, Mohammed ;
Safaei, Mahmood ;
Ghaleb, Fuad A. .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2021, 182