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Current Medical Imaging

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ISSN (Online): 1875-6603

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Review Article

Chest CT Image based Lung Disease Classification – A Review

Author(s): Shri Ramtej Kondamuri*, Venkata Sainath Gupta Thadikemalla, Gunnam Suryanarayana, Chandran Karthik, Vanga Siva Reddy, V. Bhuvana Sahithi, Y. Anitha, V. Yogitha and P. Reshma Valli

Volume 20, 2024

Published on: 16 October, 2023

Article ID: e15734056248176 Pages: 14

DOI: 10.2174/0115734056248176230923143105

open_access

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Abstract

Computed tomography (CT) scans are widely used to diagnose lung conditions due to their ability to provide a detailed overview of the body's respiratory system. Despite its popularity, visual examination of CT scan images can lead to misinterpretations that impede a timely diagnosis. Utilizing technology to evaluate images for disease detection is also a challenge. As a result, there is a significant demand for more advanced systems that can accurately classify lung diseases from CT scan images. In this work, we provide an extensive analysis of different approaches and their performances that can help young researchers to build more advanced systems. First, we briefly introduce diagnosis and treatment procedures for various lung diseases. Then, a brief description of existing methods used for the classification of lung diseases is presented. Later, an overview of the general procedures for lung disease classification using machine learning (ML) is provided. Furthermore, an overview of recent progress in ML-based classification of lung diseases is provided. Finally, existing challenges in ML techniques are presented. It is concluded that deep learning techniques have revolutionized the early identification of lung disorders. We expect that this work will equip medical professionals with the awareness they require in order to recognize and classify certain medical disorders.

Keywords: Computed tomography, Lung cancer, Pneumonia, Pulmonary embolism, Machine learning, Convolutional neural networks.

[1]
Cruz AA. Global surveillance, prevention and control of chronic respiratory diseases: A comprehensive approach. World Health Organization 2007.
[2]
Levine SM, Marciniuk DD. Global impact of respiratory disease. Chest 2022; 161(5): 1153-4.
[http://dx.doi.org/10.1016/j.chest.2022.01.014] [PMID: 35051424]
[3]
The Global Impact of Respiratory Disease. Glob Impac RespiratDis 2017.
[4]
Amani Yahiaoui OE, Yumusak N. A new method of automatic recognition for tuberculosis disease diagnosis using support vector machines. Biomed Res 2017; 28: 4208-12.
[5]
American Thoracic Society. Diagnostic standards and classification of tuberculosis in adults and children. Am J Respir Crit Care Med 2000; 161(4 Pt 1): 1376-95.
[PMID: 10764337]
[6]
Walvekar S, Shinde S. Efficient medical image segmentation of COVID-19 chest ct images based on deep learning techniques. 2021 International Conference on Emerging Smart Computing and Informatics (ESCI) 2021; 203-6.
[http://dx.doi.org/10.1109/ESCI50559.2021.9397043]
[7]
Kieu STH, Bade A, Hijazi MHA, Kolivand H. A survey of deep learning for lung disease detection on medical images: State-of-the-art, taxonomy, issues and future directions. J Imaging 2020; 6(12): 131.
[http://dx.doi.org/10.3390/jimaging6120131] [PMID: 34460528]
[8]
Toğaçar M, Ergen B, Cömert Z. Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. Biocybern Biomed Eng 2020; 40(1): 23-39.
[http://dx.doi.org/10.1016/j.bbe.2019.11.004]
[9]
Varshni D, Thakral K, Agarwal L, Nijhawan R, Mittal A. Pneumonia detection using cnn based feature extraction. 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT),. 2019 pp.1-7.
[http://dx.doi.org/10.1109/ICECCT.2019.8869364]
[10]
Ozkan H, Osman O, Sahin S. Computer aided detection of pulmonary embolism in computed tomography angiography images. 2013 International Conference on Electronics, Computer and Computation (ICECCO),. 2013 pp.355-358.
[http://dx.doi.org/10.1109/ICECCO.2013.6718301]
[11]
Balogh EP, Miller BT, Ball JR. The Diagnostic Process. Washington (DC): National Academies Press (US) 2015.
[12]
Croft P, Altman DG, Deeks JJ, et al. The science of clinical practice: Disease diagnosis or patient prognosis? Evidence about “what is likely to happen” should shape clinical practice. BMC Med 2015; 13(1): 20.
[http://dx.doi.org/10.1186/s12916-014-0265-4] [PMID: 25637245]
[13]
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: From error visibility to structural similarity. IEEE Trans Image Process 2004; 13(4): 600-12.
[http://dx.doi.org/10.1109/TIP.2003.819861] [PMID: 15376593]
[14]
Goel N, Yadav A, Singh BM. Medical image processing: A review. In: 2016 Second International Innovative Applications of Computational Intelligence on Power, Energy and Controls with their Impact on Humanity (CIPECH),. 2016, pp.57-62.
[15]
Binh NT, Khare A. Adaptive complex wavelet technique for medical image denoising. IFMBE Proc 2010; 27: 196-9.
[http://dx.doi.org/10.1007/978-3-642-12020-6_49]
[16]
Po-Hsiang Tsui , Chih-Kuang Yeh , Chih-Chung Huang . Noise-assisted correlation algorithm for suppressing noise-induced artifacts in ultrasonic Nakagami images. IEEE Trans Inf Technol Biomed 2012; 16(3): 314-22.
[http://dx.doi.org/10.1109/TITB.2011.2177851] [PMID: 22155965]
[17]
Trayush T, Bathla R, Saini S, Shukla VK. IoT in Healthcare: Challenges, Benefits, applications, and opportunities. 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) 2021, pp.107-111.
[http://dx.doi.org/10.1109/ICACITE51222.2021.9404583]
[18]
Ullah K, Shah MA, Zhang S. Effective ways to use Internet of Things in the field of medical and smart health care. 2016 International Conference on Intelligent Systems Engineering (ICISE) 2016, pp.372-379.
[http://dx.doi.org/10.1109/INTELSE.2016.7475151]
[19]
Angra S, Ahuja S. Machine learning and its applications: A review. 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC),. 2017, pp.57-60.
[http://dx.doi.org/10.1109/ICBDACI.2017.8070809]
[20]
Chellappa R, Theodoridis S, van Schaik A. Advances in machine learning and deep neural networks. Proc IEEE 2021; 109(5): 607-11.
[http://dx.doi.org/10.1109/JPROC.2021.3072172]
[21]
Shailaja K, Seetharamulu B, Jabbar MA. Machine learning in healthcare: A review. 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA),. 2018, pp.910-914.
[http://dx.doi.org/10.1109/ICECA.2018.8474918]
[22]
Ferdous M, Debnath J, Chakraborty NR. Machine learning algorithms in healthcare: A literature survey. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT),. Kharagpur, India, 2020, pp. 1-6.
[http://dx.doi.org/10.1109/ICCCNT49239.2020.9225642]
[23]
Jia X. Image recognition method based on deep learning. 2017 29th Chinese Control And Decision Conference (CCDC),. Chongqing, China, 2017, pp. 4730-4735.
[http://dx.doi.org/10.1109/CCDC.2017.7979332]
[24]
Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM 2017; 60(6): 84-90.
[http://dx.doi.org/10.1145/3065386]
[25]
Zeiler MD, Fergus R. Visualizing and understanding convolutional networks. Fleet D, Pajdla T, Schiele B, Tuytelaars T. Computer Vision – ECCV 2014 ECCV 2014 Lecture Notes in Computer Science. Cham: Springer 2014; 8689: pp. 818-33.
[http://dx.doi.org/10.1007/978-3-319-10590-1_53]
[26]
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv:14091556v6 2014.
[27]
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),. 2016, pp.770-778.
[http://dx.doi.org/10.1109/CVPR.2016.90]
[28]
Lamoureux SF, Bollmann J. Image Acquisition.Image Analysis, Sediments and Paleoenvironments. 11-34.
[29]
Nadkarni NS, Borkar S. Detection of lung cancer in ct images using image processing. 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI),. Tirunelveli, India, 2019, pp 863-866.
[http://dx.doi.org/10.1109/ICOEI.2019.8862577]
[30]
Mansoor A, Bagci U, Foster B, et al. Segmentation and image analysis of abnormal lungs at CT: Current approaches, challenges, and future trends. Radiographics 2015; 35(4): 1056-76.
[http://dx.doi.org/10.1148/rg.2015140232] [PMID: 26172351]
[31]
Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights Imaging 2018; 9(4): 611-29.
[http://dx.doi.org/10.1007/s13244-018-0639-9] [PMID: 29934920]
[32]
Nazir I, Haq IU, Khan MM, Qureshi MB, Ullah H, Butt S. Efficient pre-processing and segmentation for lung cancer detection using fused CT images. Electronics 2021; 11(1): 34.
[http://dx.doi.org/10.3390/electronics11010034]
[33]
Chaturvedi P, Jhamb A, Vanani M, Nemade V. Prediction and classification of lung cancer using machine learning techniques. IOP Conf Ser Mater SciEng. 1099: 012059.
[http://dx.doi.org/10.1088/1757-899X/1099/1/012059]
[34]
Venkatesh C, Bojja P. Lung cancer detection using bio-inspired algorithm in ct scans and secure data transmission through iot cloud. Int J Adv Comput Sci Appl 2020; 11(11)
[http://dx.doi.org/10.14569/IJACSA.2020.0111148]
[35]
Boban BM, Megalingam RK. Lung diseases classification based on machine learning algorithms and performance evaluation. 2020 International Conference on Communication and Signal Processing (ICCSP),. Chennai, India, 2020, pp. 0315-0320.
[http://dx.doi.org/10.1109/ICCSP48568.2020.9182324]
[36]
Taher F, Sammouda R. Lung cancer detection by using artificial neural network and fuzzy clustering methods. 2011 IEEE GCC Conference and Exhibition (GCC). 2011, pp. 295-298.
[http://dx.doi.org/10.1109/IEEEGCC.2011.5752535]
[37]
Potghan S, Rajamenakshi R, Bhise A. Multi-layer perceptron based lung tumor classification. 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA),. 2018, pp. 499-502.
[http://dx.doi.org/10.1109/ICECA.2018.8474864]
[38]
Song Q, Zhao L, Luo X, Dou X. Using deep learning for classification of lung nodules on computed tomography images. J Healthc Eng 2017; 2017: 1-7.
[http://dx.doi.org/10.1155/2017/8314740] [PMID: 29065651]
[39]
Bariqi Abdillah AB. Image processing based detection of lung cancer on CT scan images. J Phys Conf Ser 2017; 893: 012063.
[40]
Ajai AK, Anitha A. Clustering based lung lobe segmentation and optimization based lung cancer classification using CT images. Biomed Signal Process Control 2022; 78: 103986.
[http://dx.doi.org/10.1016/j.bspc.2022.103986]
[41]
Tsivgoulis M, Papastergiou T, Megalooikonomou V. An improved SqueezeNet model for the diagnosis of lung cancer in CT scans. Mach Learn Appl 2022; 10: 100399.
[http://dx.doi.org/10.1016/j.mlwa.2022.100399]
[42]
Pandian R, Vedanarayanan V, Ravi Kumar DNS, Rajakumar R. Detection and classification of lung cancer using CNN and Google net. Measurement. Sensors 2022; 24: 100588.
[43]
Tyagi S, Talbar SN. LCSCNet: A multi-level approach for lung cancer stage classification using 3D dense convolutional neural networks with concurrent squeeze-and-excitation module. Biomed Signal Process Control 2023; 80: 104391.
[http://dx.doi.org/10.1016/j.bspc.2022.104391]
[44]
Mohana Priya R, Venkatesan P. An efficient image segmentation and classification of lung lesions in pet and CT image fusion using DTWT incorporated SVM. Microprocess Microsyst 2021; 82: 103958.
[http://dx.doi.org/10.1016/j.micpro.2021.103958]
[45]
Asuntha A, Srinivasan A. Deep learning for lung Cancer detection and classification. Multimedia Tools Appl 2020; 79(11-12): 7731-62.
[http://dx.doi.org/10.1007/s11042-019-08394-3]
[46]
Naqi SM, Sharif M, Jaffar A. Lung nodule detection and classification based on geometric fit in parametric form and deep learning. Neural Comput Appl 2020; 32(9): 4629-47.
[http://dx.doi.org/10.1007/s00521-018-3773-x]
[47]
Marentakis P, Karaiskos P, Kouloulias V, et al. Lung cancer histology classification from CT images based on radiomics and deep learning models. Med Biol Eng Comput 2021; 59(1): 215-26.
[http://dx.doi.org/10.1007/s11517-020-02302-w] [PMID: 33411267]
[48]
Choe J, Hwang HJ, Seo JB, et al. Content-based image retrieval by using deep learning for interstitial lung disease diagnosis with chest CT. Radiology 2022; 302(1): 187-97.
[http://dx.doi.org/10.1148/radiol.2021204164] [PMID: 34636634]
[49]
Yadav P, Menon N, Ravi V, Vishvanathan S. Lung-GANs: Unsupervised representation learning for lung disease classification using chest CT and X-Ray images. IEEE Trans Eng Manage 2023; 70(8): 2774-86.
[http://dx.doi.org/10.1109/TEM.2021.3103334]
[50]
Xie Y, Xia Y, Zhang J, et al. Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on Chest CT. IEEE Trans Med Imaging 2019; 38(4): 991-1004.
[http://dx.doi.org/10.1109/TMI.2018.2876510] [PMID: 30334786]
[51]
Xie Y, Zhang J, Xia Y. Semi-supervised adversarial model for benign–malignant lung nodule classification on chest CT. Med Image Anal 2019; 57: 237-48.
[http://dx.doi.org/10.1016/j.media.2019.07.004] [PMID: 31352126]
[52]
Venkatesh C, Ramana K, Lakkisetty SY, Band SS, Agarwal S, Mosavi A. A neural network and optimization based lung cancer detection system in CT images. Front Public Health 2022; 10: 769692.
[http://dx.doi.org/10.3389/fpubh.2022.769692] [PMID: 35747775]
[53]
Agrawal H. Pneumonia detection using image processing and deep learning. 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS),. 2021, pp.67-73.
[http://dx.doi.org/10.1109/ICAIS50930.2021.9395895]
[54]
Zhang D, Ren F, Li Y, Na L, Ma Y. Pneumonia detection from chest x-ray images based on convolutional neural network. Electronics 2021; 10(13): 1512.
[http://dx.doi.org/10.3390/electronics10131512]
[55]
Chagas JVSD, de A Rodrigues D, Ivo RF, Hassan MM, de Albuquerque VHC, Filho PPR. A new approach for the detection of pneumonia in children using CXR images based on an real-time IoT system. J Real-Time Image Process 2021; 18(4): 1099-114.
[http://dx.doi.org/10.1007/s11554-021-01086-y] [PMID: 33747237]
[56]
Goyal S, Singh R. Detection and classification of lung diseases for pneumonia and COVID-19 using machine and deep learning techniques. J Ambient Intell Humaniz Comput 2021; 14(4): 3239-59.
[PMID: 34567277]
[57]
Chouhan V, Singh SK, Khamparia A, et al. A novel transfer learning based approach for pneumonia detection in chest x-ray images. Appl Sci 2020; 10(2): 559.
[http://dx.doi.org/10.3390/app10020559]
[58]
Al Mamlook RE, Chen S, Bzizi HF. Investigation of the performance of machine learning classifiers for pneumonia detection in chest X-ray Images. 2020 IEEE International Conference on Electro Information Technology (EIT),. Chicago, IL, USA, 2020, pp. 098-104.
[http://dx.doi.org/10.1109/EIT48999.2020.9208232]
[59]
Rajaraman S, Candemir S, Kim I, Thoma G, Antani S. Visualization and interpretation of convolutional neural network predictions in detecting pneumonia in pediatric chest radiographs. Appl Sci 2018; 8(10): 1715.
[http://dx.doi.org/10.3390/app8101715] [PMID: 32457819]
[60]
Wang Q, Yang D, Li Z, Zhang X, Liu C. Deep regression via multi-channel multi-modal learning for pneumonia screening. IEEE Access 2020; 8: 78530-41.
[http://dx.doi.org/10.1109/ACCESS.2020.2990423]
[61]
Cano-Espinosa C, Cazorla M, González G. Computer aided detection of pulmonary embolism using multi-slice multi-axial segmentation. Appl Sci 2020; 10(8): 2945.
[http://dx.doi.org/10.3390/app10082945]
[62]
Ming JTC, Noor NM, Rijal OM, Kassim RM, Yunus A. Lung disease classification using different deep learning architectures and principal component analysis. 2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS),. Kuching, Malaysia, 2018, pp. 187-190.
[http://dx.doi.org/10.1109/ICBAPS.2018.8527385]
[63]
Huhtanen H, Nyman M, Mohsen T, Virkki A, Karlsson A, Hirvonen J. Automated detection of pulmonary embolism from CT-angiograms using deep learning. BMC Med Imaging 2022; 22(1): 43.
[http://dx.doi.org/10.1186/s12880-022-00763-z] [PMID: 35282821]
[64]
Myers MH, Beliaev I, Lin KI. Machine learning techniques in detecting of pulmonary embolisms. 2007 International Joint Conference on Neural Networks,. 2007, pp.385-390.
[http://dx.doi.org/10.1109/IJCNN.2007.4370987]
[65]
Ajmera P, Kharat A, Seth J, et al. A deep learning approach for automated diagnosis of pulmonary embolism on computed tomographic pulmonary angiography. BMC Med Imaging 2022; 22(1): 195.
[http://dx.doi.org/10.1186/s12880-022-00916-0] [PMID: 36368975]
[66]
Ma X, Ferguson EC, Jiang X, Savitz SI, Shams S. A multitask deep learning approach for pulmonary embolism detection and identification. Sci Rep 2022; 12(1): 13087.
[http://dx.doi.org/10.1038/s41598-022-16976-9] [PMID: 35906477]
[67]
Wang L, Lin ZQ, Wong A. COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci Rep 2020; 10(1): 19549.
[http://dx.doi.org/10.1038/s41598-020-76550-z] [PMID: 33177550]
[68]
Yang Y, Feng X, Chi W, et al. Deep learning aided decision support for pulmonary nodules diagnosing: A review. J Thorac Dis 2018; 10(S7) (7): S867-75.
[http://dx.doi.org/10.21037/jtd.2018.02.57] [PMID: 29780633]
[69]
Qin C, Yao D, Shi Y, Song Z. Computer-aided detection in chest radiography based on artificial intelligence: A survey. Biomed Eng Online 2018; 17(1): 113.
[http://dx.doi.org/10.1186/s12938-018-0544-y] [PMID: 30134902]
[70]
Azuaje F. Artificial intelligence for precision oncology: Beyond patient stratification. NPJ Precis Oncol 2019; 3(1): 6.
[http://dx.doi.org/10.1038/s41698-019-0078-1] [PMID: 30820462]
[71]
Tan Y, Guo P, Mann H, et al. Assessing the effect of CT slice interval on unidimensional, bidimensional and volumetric measurements of solid tumours. Cancer Imaging 2012; 12(3): 497-505.
[http://dx.doi.org/10.1102/1470-7330.2012.0046] [PMID: 23113962]
[72]
Yasaka K, Akai H, Mackin D, et al. Precision of quantitative computed tomography texture analysis using image filtering. Medicine 2017; 96(21): e6993.
[http://dx.doi.org/10.1097/MD.0000000000006993] [PMID: 28538408]
[73]
Kim H, Park CM, Lee M, Park SJ, Song YS, Lee JH, et al. Impact of reconstruction algorithms on ct radiomic features of pulmonary tumors: Analysis of intra- and inter-reader variability and inter-reconstruction algorithm variability. PLoS One 2016; 11: e0164924.
[74]
Shafiq-ul-Hassan M, Zhang GG, Latifi K, et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys 2017; 44(3): 1050-62.
[http://dx.doi.org/10.1002/mp.12123] [PMID: 28112418]
[75]
Girardi D, Küng J, Kleiser R, et al. Interactive knowledge discovery with the doctor-in-the-loop: A practical example of cerebral aneurysms research. Brain Inform 2016; 3(3): 133-43.
[http://dx.doi.org/10.1007/s40708-016-0038-2] [PMID: 27747590]
[76]
Yu MK, Ma J, Fisher J, Kreisberg JF, Raphael BJ, Ideker T. Visible machine learning for biomedicine. Cell 2018; 173(7): 1562-5.
[http://dx.doi.org/10.1016/j.cell.2018.05.056] [PMID: 29906441]

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