Detection of Lung Diseases for Pneumonia, Tuberculosis, and COVID-19 with Artificial Intelligence Tools
Abstract
Chest X-ray imaging is a low-cost, easy way to diagnose lung abnormalities caused by infectious diseases such as COVID-19, pneumonia, or tuberculosis. The primary objective of the study is to carefully analyse and evaluate several classification strategies to determine which technique based on machine learning or deep learning would be more useful for detecting lung infectious illness using chest X-rays of three pulmonary infectious diseases: pneumonia, TB, and COVID-19. To notify physicians and radiologists of probable aberrant results, the performance of numerous classifiers—deep learning algorithms (CNN) and conventional machine learning algorithms—for distinguishing between normal and pathological chest radiographs was assessed and compared. The comparative analysis is based on three important criteria: the performance metrics (precision, accuracy, recall, and f1-score), minimising overfitting, and reducing false negative and false positive counts. Results of evaluation show convolutional neural network model accuracy across training and test samples was 94.71% and 90.22% for dataset I, 96.31% and 95.60% for dataset II, and 99.01% and 99.04% for dataset III, respectively, which is better than the conventional ML models. The experimental results in this paper also show that a deep learning framework such as CNN outperforms traditional machine learning approaches, viz., support vector machines, logistic regression, k-nearest neighbours, Naive Bayes, decision trees, and random forests on large X-ray image datasets, as it also shows better results for precision, F1 score, and recall, minimum overfitting, and a reduced number of false negative and false positive counts for pneumonia, TB, and COVID-19 lung diseases.
References
[1]
Santosh KC, Rasmussen N, Mamun M, and Aryal S A systematic review on cough sound analysis for Covid-19 diagnosis and screening: is my cough sound COVID-19? PeerJ Comput Sci. 2022
[2]
Tang YX et al. Automated abnormality classification of chest radiographs using deep convolutional neural networks NPJ Digit Med 2020
[3]
Singh S and Tripathi BK Pneumonia classification using quaternion deep learning Multimed Tools Appl 2022 81 2 1743-1764
[4]
Barhoom AMA, Samy P, and Naser SA Diagnosis of pneumonia using deep learning Int J Acad Eng Res 2022 6 2 48-68
[5]
Wang Q, Yang D, Li Z, Zhang X, and Liu C Deep regression via multi-channel multi-modal learning for pneumonia screening IEEE Access. 2020 8 78530-78541
[6]
Henderson J and Santosh K Analyzing chest X-ray to detect the evidence of lung abnormality due to infectious disease Commun Comput Inform Sci 2023
[7]
Ling G and Cao C Atomatic detection and diagnosis of severe viral pneumonia CT images based on LDA-SVM IEEE Sens J 2020 20 20 11927-11934
[8]
Santosh K and Ghosh S CheXNet for the evidence of Covid-19 using 2.3K positive chest X-rays’ Commun Comput Inform Sci. 2022 1576 CCIS 33-41
[9]
Ibrahim DM, Elshennawy NM, Sarhan AM. ‘Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID- 19. The COVID-19 resource centre is hosted on Elsevier Connect, the company ’ s public news and information ’, no. January, 2020.
[10]
Bhapkar HR, Mahalle PN, Dey N, and Santosh KC Revisited COVID-19 mortality and recovery rates: are we missing recovery time period? J Med Syst. 2020
[11]
Mohan Y and Tripathi V Comparative analysis of facial expression detection techniques based on neural network Int J Eng Technol 2018 7 4 38
[12]
Santosh KC COVID-19 prediction models and unexploited data J Med Syst. 2020
[13]
Mukherjee H et al. ‘Deep neural network for pneumonia detection using chest X-Rays. In: Communications in Computer and Information Science 2021 New York Springer Science and Business Media Deutschland GmbH
[14]
Hassantabar S, Ahmadi M, Chaos AS, Fractals S, undefined 2020, ‘Diagnosis and detection of infected tissue of COVID-19 patients based on lung X-ray image using convolutional neural network approaches. Elsevier, Accessed: Oct. 20, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S096007792030566X
[15]
Santosh KC, Ghosh S, and Ghoshroy D Deep learning for Covid-19 screening using chest X-rays in 2020 a systematic review Intern J Pattern Recognit Artif Intell 2022
[16]
Santosh K, Allu S, Rajaraman S, and Antani S Advances in deep learning for tuberculosis screening using chest X-rays: the last 5 years review J Med Syst 2022
[17]
Santosh KC and Ghosh S Covid-19 versus lung cancer: analyzing chest CT images using deep ensemble neural network Int J Artif Intell Tools. 2022
[18]
Mahbub MK, Biswas M, Gaur L, Alenezi F, and Santosh KC Deep features to detect pulmonary abnormalities in chest X-rays due to infectious diseaseX: Covid-19, pneumonia, and tuberculosis Inf Sci (N Y) 2022 592 389-401
[19]
Bharati S, Podder P, and Mondal MRH Hybrid deep learning for detecting lung diseases from X-ray images Inform Med Unlocked 2020 20
[20]
Liang G and Zheng L A transfer learning method with deep residual network for pediatric pneumonia diagnosis Comput Methods Progr Biomed 2020
[21]
Jaiswal AK, Tiwari P, Kumar S, Gupta D, Khanna A, and Rodrigues JJPC Identifying pneumonia in chest X-rays: A deep learning approach Measurement (Lond) 2019 145 511-518
[22]
Kamal M, Chowdhury L, ND on Systems, undefined Man, and undefined 2021, ‘Explainable ai to analyze outcomes of spike neural network in covid-19 chest x-rays. ieeexplore.ieee.org, Accessed: Jun. 28, 2023. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9658745/
[23]
Ortiz-Toro C, García-Pedrero A, Lillo-Saavedra M, and Gonzalo-Martín C Automatic detection of pneumonia in chest X-ray images using textural features Comput Biol Med 2022
[24]
‘CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning’. 2019.
[25]
Das D, Santosh KC, and Pal U Cross-population train/test deep learning model: abnormality screening in chest x-rays Proc IEEE Symp Comput-Based Med Syst 2020
[26]
Santosh KC AI-driven tools for coronavirus outbreak: need of active learning and cross-population train/test models on multitudinal/multimodal data J Med Syst. 2020
[27]
Qian X et al. M3Lung-sys: a deep learning system for multi-class lung pneumonia screening from CT imaging IEEE J Biomed Health Inform 2020 24 12 3539-3550
[28]
Santosh KC, Dhar MK, Rajbhandari R, and Neupane A Deep neural network for foreign object detection in chest X-rays Proc IEEE Symp Comput-Based Med Syst 2020
[29]
Muhammad Y, Alshehri MD, Alenazy WM, Vinh Hoang T, and Alturki R Identification of pneumonia disease applying an intelligent computational framework based on deep learning and machine learning techniques Mobile Inform Syst. 2021
[30]
Das D, Santosh KC, and Pal U Inception-based deep learning architecture for tuberculosis screening using chest x-rays Proc Int Conf Pattern Recogn 2020
[31]
Kundu R, Das R, Geem ZW, Han GT, and Sarkar R Pneumonia detection in chest X-ray images using an ensemble of deep learning models PLoS One 2021
[32]
Gm H, Gourisaria MK, Rautaray SS, and Pandey M Pneumonia detection using CNN through chest X-ray J Eng Sci Technol 2021 16 1 861-876
[33]
Mukherjee H, Ghosh S, Dhar A, Obaidullah SM, Santosh KC, and Roy K Deep neural network to detect COVID-19: one architecture for both CT Scans and Chest X-rays Appl Intell 2021
[34]
Meng Z, Meng L, and Tomiyama H Pneumonia diagnosis on chest X-rays with machine learning Procedia Comput Sci 2021 187 42-51
[35]
Yaseliani M, Hamadani AZ, Maghsoodi AI, and Mosavi A Pneumonia detection proposing a hybrid deep convolutional neural network based on two parallel visual geometry group architectures and machine learning classifiers IEEE Access 2022 10 62110-62128
[36]
Varshni D, Thakral K, Agarwal L, Nijhawan R, Mittal A. ‘Pneumonia Detection Using CNN based Feature Extraction. Proceedings of 2019 3rd IEEE International Conference on Electrical, Computer and Communication Technologies. ICECCT 2019. 2019.
[37]
Mahbub MK, Hossain Zamil MZ, Mozid Miah MA, Ghose P, Biswas M, Santosh KC. ‘MobApp4InfectiousDisease: Classify COVID-19, Pneumonia, and Tuberculosis. In: Proceedings IEEE Symposium on Computer-Based Medical Systems, 2022.
[38]
‘Chest X-Ray Images (Pneumonia) | Kaggle’. https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia (accessed Mar. 01, 2023).
[39]
Long A et al. ‘The technology behind TB DEPOT: a novel public analytics platform integrating tuberculosis clinical, genomic, and radiological data for visual and statistical exploration J Am Med Inform Assoc 2021
[40]
Rahman T et al. Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization IEEE Access 2020 8 191586-191601
[41]
‘Tuberculosis (TB) Chest X-ray Database | IEEE DataPort’. https://ieee-dataport.org/documents/tuberculosis-tb-chest-x-ray-database (Accessed Feb. 26, 2023).
[42]
‘Tuberculosis (TB) Chest X-ray Database | Kaggle’. https://www.kaggle.com/datasets/tawsifurrahman/tuberculosis-tb-chest-xray-dataset (Accessed Feb. 26, 2023).
[43]
Jaeger S, Candemir S, S. A.- imaging in medicine, and undefined 2014, ‘Two public chest X-ray datasets for computer-aided screening of pulmonary diseases’, ncbi.nlm.nih.gov, Accessed: Feb. 26, 2023. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4256233/
[44]
Cohen JP, Morrison P, Dao L, Roth K, Duong TQ, Ghassemi M. ‘COVID-19 Image Data Collection: Prospective Predictions Are the Future’, Jun. 2020, Accessed: Mar. 01, 2023. [Online]. Available: http://arxiv.org/abs/2006.11988
[45]
Ng MY et al. Imaging profile of the covid-19 infection: radiologic findings and literature review Radiol Cardiothorac Imaging 2020
[46]
Santosh K and Ghosh S Covid-19 imaging tools: how big data is big? J Med Syst 2021
[47]
Albawi S, Mohammed TA, and Al-Zawi S ‘Understanding of a convolutional neural network Proc 2017 Int Conf Eng Technol. 2018
[48]
Gu J et al. Recent advances in convolutional neural networks Pattern Recognit 2018 77 354-377
[49]
Rasheed J, Hameed AA, Djeddi C, Jamil A, and Al-Turjman F ‘A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images Interdiscip Sci-Comput Life Sci. 2021 13 1 103-117
[50]
Nusinovici S et al. Logistic regression was as good as machine learning for predicting major chronic diseases J Clin Epidemiol 2020 122 56-69
[51]
Erdaw Y and Tachbele E <p>Machine learning model applied on chest X-ray images enables automatic detection of COVID-19 cases with high accuracy</p> Int J Gen Med 2021 14 4923-4931
[52]
Wu X et al. Top 10 algorithms in data mining Knowl Inf Syst 2008 14 1 1-37
[53]
Murphy KP. Naive Bayes classifiers. University of British Columbia, vol. 18, no. 60. 2006. pp 1–8.
[54]
Taheri S and Mammadov M Learning the naive Bayes classifier with optimization models Int J Appl Math Comput Sci 2013 23 4 787-795
[55]
Song Y-Y and Lu Y Decision tree methods: applications for classification and prediction Psychiatry 2015 27 2 130-135
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Published: 06 March 2024
Accepted: 10 January 2024
Received: 23 March 2023
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