research-article

Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI

Authors:

Mohan Bhandari,

Tej Bahadur Shahi,

Arjun NeupaneAuthors Info & Claims

Volume 150, Issue C

https://doi.org/10.1016/j.compbiomed.2022.106156

Published: 01 November 2022 Publication History

Abstract

Chest X-ray (CXR) images are considered useful to monitor and investigate a variety of pulmonary disorders such as COVID-19, Pneumonia, and Tuberculosis (TB). With recent technological advancements, such diseases may now be recognized more precisely using computer-assisted diagnostics. Without compromising the classification accuracy and better feature extraction, deep learning (DL) model to predict four different categories is proposed in this study. The proposed model is validated with publicly available datasets of 7132 chest x-ray (CXR) images. Furthermore, results are interpreted and explained using Gradient-weighted Class Activation Mapping (Grad-CAM), Local Interpretable Modelagnostic Explanation (LIME), and SHapley Additive exPlanation (SHAP) for better understandably. Initially, convolution features are extracted to collect high-level object-based information. Next, shapely values from SHAP, predictability results from LIME, and heatmap from Grad-CAM are used to explore the black-box approach of the DL model, achieving average test accuracy of 94.31 ± 1.01% and validation accuracy of 94.54 ± 1.33 for 10-fold cross validation. Finally, in order to validate the model and qualify medical risk, medical sensations of classification are taken to consolidate the explanations generated from the eXplainable Artificial Intelligence (XAI) framework. The results suggest that XAI and DL models give clinicians/medical professionals persuasive and coherent conclusions related to the detection and categorization of COVID-19, Pneumonia, and TB.

Highlights

•

A light weight CNN to detect infection on CXR images.

•

Explanatory Classification of CXR Images into COVID-19, Pneumonia and Tuberculosis.

•

Exploring the Black box approach of CNN using XAI.

•

Performance comparison with other state-of-the-art methods.

References

[1]

Disease G., Pocket Guide to Copd Diagnosis, Management and Prevention: A Guide for Healthcare Professsionals, Independently Published, 2021.

[2]

Visca D., Ong C., Tiberi S., Centis R., D’Ambrosio L., Chen B., Mueller J., Mueller P., Duarte R., Dalcolmo M., Sotgiu G., Migliori G.B., Goletti D., Tuberculosis and COVID-19 interaction: A review of biological, clinical and public health effects, Pulmonology 27 (2021).

[3]

Islam S.R., Maity S.P., Ray A.K., Mandal M., Deep learning on compressed sensing measurements in pneumonia detection, Int. J. Imaging Syst. Technol. (2022).

[4]

van’t Hoog A.H., Meme H.K., Laserson K.F., Agaya J.A., Muchiri B.G., Githui W.A., Odeny L.O., Marston B.J., Borgdorff M.W., Screening strategies for tuberculosis prevalence surveys: the value of chest radiography and symptoms, PLoS One 7 (7) (2012).

[5]

Kallander K., Burgess D.H., Qazi S.A., Early identification and treatment of pneumonia: a call to action, Lancet. Glob. Health 4 (1) (2016).

[6]

Asif H.M., Hashmi H.A.S., Early detection of COVID-19, Front. Med. (2020).

[7]

Voulodimos A., Doulamis N., Doulamis A., Protopapadakis E., Deep learning for computer vision: A brief review, Comput. Intell. Neurosci. 2018 (2018).

Digital Library

[8]

Shahi T.B., Sitaula C., Neupane A., Guo W., Fruit classification using attention-based MobileNetV2 for industrial applications, Plos One 17 (2) (2022).

[9]

Mishra B., Shahi T.B., Deep learning-based framework for spatiotemporal data fusion: an instance of Landsat 8 and Sentinel 2 NDVI, J. Appl. Remote Sens. 15 (3) (2021).

[10]

Bhandari M., Parajuli P., Chapagain P., Gaur L., Evaluating performance of adam optimization by proposing energy index, in: Santosh K., Hegadi R., Pal U. (Eds.), Recent Trends in Image Processing and Pattern Recognition, Springer International Publishing, Cham, 2022, pp. 156–168.

[11]

Gaur L., Bhandari M., Razdan T., Development of Image Translating Model to Counter Adversarial Attacks, Taylor and Francis, 2022.

[12]

Sitaula C., Basnet A., Mainali A., Shahi T., Deep learning-based methods for sentiment analysis on nepali COVID-19-related tweets, Comput. Intell. Neurosci. 2021 (2021).

[13]

Mohsen H., El-Dahshan E.-S.A., El-Horbaty E.-S.M., Salem A.-B.M., Classification using deep learning neural networks for brain tumors, Future Comput. Inform. J. 3 (1) (2018) 68–71.

[14]

Gaur L., Bhandari M., Shikhar B.S., Nz J., Shorfuzzaman M., Masud M., Explanation-driven HCI model to examine the mini-mental state for alzheimer’s disease, ACM Trans. Multimedia Comput. Commun. Appl. (2022),.

Digital Library

[15]

Sitaula C., Hossain M.B., Attention-based VGG-16 model for COVID-19 chest X-ray image classification, Appl. Intell. 51 (5) (2021) 2850–2863.

[16]

Homayounieh F., Digumarthy S., Ebrahimian S., Rueckel J., Hoppe B.F., Sabel B.O., Conjeti S., Ridder K., Sistermanns M., Wang L., Preuhs A., Ghesu F., Mansoor A., Moghbel M., Botwin A., Singh R., Cartmell S., Patti J., Huemmer C., Fieselmann A., Joerger C., Mirshahzadeh N., Muse V., Kalra M., An artificial intelligence–based chest X-ray model on human nodule detection accuracy from a multicenter study, JAMA Netw. Open 4 (12) (2021),. e2141096.

[17]

Hinton G.E., Salakhutdinov R.R., Reducing the dimensionality of data with neural networks, Science 313 (5786) (2006) 504–507.

[18]

Gu J., Wang Z., Kuen J., Ma L., Shahroudy A., Shuai B., Liu T., Wang X., Wang G., Cai J., et al., Recent advances in convolutional neural networks, Pattern Recognit. 77 (2018) 354–377.

Digital Library

[19]

Mahbub M.K., Biswas M., Gaur L., Alenezi F., Santosh K., Deep features to detect pulmonary abnormalities in chest X-rays due to infectious diseasex: Covid-19, pneumonia, and tuberculosis, Inform. Sci. 592 (2022) 389–401.

[20]

Liu C., Cao Y., Alcantara M., Liu B., Brunette M., Peinado J., Curioso W., TX-CNN: Detecting tuberculosis in chest X-ray images using convolutional neural network, in: 2017 IEEE International Conference on Image Processing, ICIP, 2017, pp. 2314–2318,.

Digital Library

[21]

Ahsan M.M., Gupta K.D., Islam M.M., Sen S., Rahman M., Shakhawat Hossain M., et al., Covid-19 symptoms detection based on nasnetmobile with explainable ai using various imaging modalities, Mach. Learn. Knowl. Extr. 2 (4) (2020) 490–504.

[22]

Abbas A., Abdelsamea M.M., Gaber M.M., Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network, Appl. Intell. 51 (2) (2021) 854–864.

Digital Library

[23]

Pasa F., Golkov V., Pfeiffer F., Cremers D., Pfeiffer D., Efficient deep network architectures for fast chest X-ray tuberculosis screening and visualization, Sci. Rep. 9 (1) (2019) 1–9.

[24]

Rahman T., Chowdhury M.E., Khandakar A., Islam K.R., Islam K.F., Mahbub Z.B., Kadir M.A., Kashem S., Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray, Appl. Sci. 10 (9) (2020) 3233.

[25]

Mediouni M., Schlatterer D.R., Madry H., Cucchiarini M., Rai B., A review of translational medicine. The future paradigm: how can we connect the orthopedic dots better?, Curr. Med. Res. Opin. 34 (7) (2018) 1217–1229,. PMID: 28952378.

[26]

Mediouni M., Madiouni R., Gardner M., Vaughan N., Translational medicine: challenges and new orthopaedic vision (mediouni-model), Curr. Orthop. Pract. 31 (2) (2020) 196–200.

[27]

Antoniadi A.M., Du Y., Guendouz Y., Wei L., Mazo C., Becker B.A., Mooney C., Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: A systematic review, Appl. Sci. 11 (11) (2021) URL https://www.mdpi.com/2076-3417/11/11/5088.

[28]

Sitaula C., Shahi T., Aryal S., Marzbanrad F., Fusion of multi-scale bag of deep visual words features of chest X-ray images to detect COVID-19 infection, Sci. Rep. 11 (2021).

[29]

Satu M.S., Howlader K.C., Mahmud M., Kaiser M.S., Shariful Islam S.M., Quinn J.M.W., Alyami S.A., Moni M.A., Short-term prediction of COVID-19 cases using machine learning models, Appl. Sci. 11 (9) (2021) URL https://www.mdpi.com/2076-3417/11/9/4266.

[30]

Qaqos N.N., Kareem O.S., Covid-19 diagnosis from chest x-ray images using deep learning approach, in: 2020 International Conference on Advanced Science and Engineering, ICOASE, IEEE, 2020, pp. 110–116.

[31]

Ahsan M., Gomes R., Denton A., Application of a convolutional neural network using transfer learning for tuberculosis detection, in: 2019 IEEE International Conference on Electro Information Technology, EIT, 2019, pp. 427–433,.

[32]

Shastri S., Kansal I., Kumar S., Singh K., Popli R., Mansotra V., Cheximagenet: a novel architecture for accurate classification of Covid-19 with chest x-ray digital images using deep convolutional neural networks, Health Technol. (2022) 1–12.

[33]

Rahimzadeh M., Attar A., A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of xception and ResNet50V2, Inf. Med. Unlocked 19 (2020).

[34]

Colombo Filho M.E., Mello Galliez R., Andrade Bernardi F., Oliveira L.L.d., Kritski A., Koenigkam Santos M., Alves D., Preliminary results on pulmonary tuberculosis detection in chest x-ray using convolutional neural networks, in: International Conference on Computational Science, Springer, 2020, pp. 563–576.

[35]

Shelke A., Inamdar M., Shah V., Tiwari A., Hussain A., Chafekar T., Mehendale N., Chest X-ray classification using deep learning for automated COVID-19 screening, SN Comput. Sci. 2 (4) (2021) 1–9.

Digital Library

[36]

Al-Timemy A.H., Khushaba R.N., Mosa Z.M., Escudero J., An efficient mixture of deep and machine learning models for covid-19 and tuberculosis detection using x-ray images in resource limited settings, in: Artificial Intelligence for COVID-19, Springer, 2021, pp. 77–100.

[37]

Kim S., Rim B., Choi S., Lee A., Min S., Hong M., Deep learning in multi-class lung diseases; classification on chest X-ray images, Diagnostics 12 (4) (2022) URL https://www.mdpi.com/2075-4418/12/4/915.

[38]

Ragab M., Alshehri S., Alhakamy N., Mansour R., Koundal D., Multiclass classification of chest X-Ray images for the prediction of COVID-19 using capsule network, Comput. Intell. Neurosci. 2022 (2022) 1–8.

[39]

R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, Grad-cam: Visual explanations from deep networks via gradient-based localization, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 618–626.

[40]

Ahsan M.M., Nazim R., Siddique Z., Huebner P., Detection of COVID-19 patients from CT scan and chest X-ray data using modified MobileNetV2 and LIME, Healthcare 9 (9) (2021) URL https://www.mdpi.com/2227-9032/9/9/1099.

[41]

Chetoui M., Akhloufi M.A., Yousefi B., Bouattane E.M., Explainable COVID-19 detection on chest X-rays using an end-to-end deep convolutional neural network architecture, Big Data Cogn. Comput. 5 (4) (2021) 73.

[42]

J. Deng, A large-scale hierarchical image database, in: Proc. of IEEE Computer Vision and Pattern Recognition, 2009, 2009.

[43]

Li L., Qin L., Xu Z., Yin Y., Wang X., Kong B., Bai J., Lu Y., Fang Z., Song Q., et al., Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT, Radiology (2020).

[44]

Kermany D., Zhang K., Goldbaum M., Large dataset of labeled optical coherence tomography (OCT) and chest X-Ray images, Cell (2018).

[45]

Rahman T., Khandakar A., Kadir M.A., Islam K.R., Islam K.F., Mazhar R., Hamid T., Islam M.T., Kashem S., Mahbub Z.B., Ayari M.A., Chowdhury M.E.H., Reliable tuberculosis detection using chest X-Ray with deep learning, segmentation and visualization, IEEE Access 8 (2020) 191586–191601.

[46]

Patel P., Chest X-ray (Covid-19 and pneumonia), Kaggle (2020) URL https://www.kaggle.com/datasets/prashant268/chest-xray-covid19-pneumonia.

[47]

van der Velden B.H., Kuijf H.J., Gilhuijs K.G., Viergever M.A., Explainable artificial intelligence (XAI) in deep learning-based medical image analysis, Med. Image Anal. 79 (2022),.

[48]

Lundberg S.M., Lee S.-I., A unified approach to interpreting model predictions, in: Guyon I., Luxburg U.V., Bengio S., Wallach H., Fergus R., Vishwanathan S., Garnett R. (Eds.), Advances in Neural Information Processing Systems 30, Curran Associates, Inc., 2017, pp. 4765–4774. URL http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf.

[49]

Chollet F., et al., Keras, 2015, URL https://github.com/fchollet/keras.

[50]

Van Rossum G., Drake F.L., Python 3 Reference Manual, CreateSpace, Scotts Valley, CA, 2009.

[51]

Carneiro T., Medeiros Da NóBrega R.V., Nepomuceno T., Bian G.-B., De Albuquerque V.H.C., Filho P.P.R., Performance analysis of google colaboratory as a tool for accelerating deep learning applications, IEEE Access 6 (2018) 61677–61685.

[52]

Khan A.I., Shah J.L., Bhat M.M., Coronet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images, Comput. Methods Programs Biomed. 196 (2020).

Digital Library

Cited By

Nahiduzzaman MAhamed MNaznine MKarim MKibria HAyari MKhandakar AAshraf AAhsan MHaider J(2025)An automated waste classification system using deep learning techniquesKnowledge-Based Systems10.1016/j.knosys.2025.113028310:COnline publication date: 15-Feb-2025
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Hassan SAbdulkadir SZahid MAl-Selwi S(2025)Local interpretable model-agnostic explanation approach for medical imaging analysisComputers in Biology and Medicine10.1016/j.compbiomed.2024.109569185:COnline publication date: 1-Feb-2025
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Ahamed MShafi FNahiduzzaman MAyari MKhandakar A(2025)Interpretable deep learning architecture for gastrointestinal disease detectionComputers in Biology and Medicine10.1016/j.compbiomed.2024.109503185:COnline publication date: 1-Feb-2025
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Index Terms

Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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cover image Computers in Biology and Medicine

Computers in Biology and Medicine Volume 150, Issue C

Nov 2022

1546 pages

ISSN:0010-4825

Issue’s Table of Contents

Elsevier Ltd.

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Pergamon Press, Inc.

United States

Publication History

Published: 01 November 2022

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Cited By

Nahiduzzaman MAhamed MNaznine MKarim MKibria HAyari MKhandakar AAshraf AAhsan MHaider J(2025)An automated waste classification system using deep learning techniquesKnowledge-Based Systems10.1016/j.knosys.2025.113028310:COnline publication date: 15-Feb-2025
https://dl.acm.org/doi/10.1016/j.knosys.2025.113028
Hassan SAbdulkadir SZahid MAl-Selwi S(2025)Local interpretable model-agnostic explanation approach for medical imaging analysisComputers in Biology and Medicine10.1016/j.compbiomed.2024.109569185:COnline publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.109569
Ahamed MShafi FNahiduzzaman MAyari MKhandakar A(2025)Interpretable deep learning architecture for gastrointestinal disease detectionComputers in Biology and Medicine10.1016/j.compbiomed.2024.109503185:COnline publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.109503
ŞAHiN EArslan NÖzdemir D(2025)Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learningNeural Computing and Applications10.1007/s00521-024-10437-237:2(859-965)Online publication date: 1-Jan-2025
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Srikanth PBehera CRouthu S(2025)CovidSafe: A Deep Learning Framework for Covid Detection Using Multi-modal ApproachNew Generation Computing10.1007/s00354-024-00289-843:1Online publication date: 1-Feb-2025
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Sun QAkman ASchuller B(2024)Explainable Artificial Intelligence for Medical Applications: A ReviewACM Transactions on Computing for Healthcare10.1145/37093676:2(1-31)Online publication date: 21-Dec-2024
https://dl.acm.org/doi/10.1145/3709367
Antamis TDrosou AVafeiadis TNizamis AIoannidis DTzovaras D(2024)Interpretability of deep neural networksNeurocomputing10.1016/j.neucom.2024.128204601:COnline publication date: 7-Oct-2024
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Ahamed MNahiduzzaman MIslam MNaznine MArselene Ayari MKhandakar AHaider J(2024)Detection of various gastrointestinal tract diseases through a deep learning method with ensemble ELM and explainable AIExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124908256:COnline publication date: 5-Dec-2024
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Uma Maheswari VStephe SAluvalu RThirumalraj AMohanty S(2024)Chaotic Satin Bowerbird Optimizer Based Advanced AI Techniques for Detection of COVID-19 Diseases from CT Scans ImagesNew Generation Computing10.1007/s00354-024-00279-w42:5(1065-1087)Online publication date: 1-Dec-2024
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Alhadad AMostafa REl-Bakry H(2023)Deep SVDD and Transfer Learning for COVID-19 Diagnosis Using CT ImagesComputational Intelligence and Neuroscience10.1155/2023/60709702023Online publication date: 1-Jan-2023
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