[
Ashok, K. and Gopikrishnan, S. (2023). Improving security performance of healthcare data in the internet of medical things using a hybrid metaheuristic model, International Journal of Applied Mathematics and Computer Science 33(4): 623–636, DOI: 10.34768/amcs-2023-0044.
]Search in Google Scholar
[
Behzadi-Khormouji, H., Rostami, H., Salehi, S., Derakhshande-Rishehri, T., Masoumi, M., Salemi, S. and Batouli, A. (2020). Deep learning, reusable and problem-based architectures for detection of consolidation on chest X-ray images, Computer Methods and Programs in Biomedicine 185: 105162.
]Search in Google Scholar
[
Bernardi, D., Caumo, F.,Macaskill, P., Ciatto, S., Pellegrini,M., Brunelli, S. and Houssami, N. (2014). Effect of integrating 3d-mammography (digital breast tomosynthesis) with 2d-mammography on radiologists’ true-positive and false-positive detection in a population breast screening trial, European Journal of Cancer 50(7): 1232–1238.
]Search in Google Scholar
[
Casalino, G., Castellano, G., Hryniewicz, O., Leite, D., Opara, K., Radziszewska, W. and Kaczmarek-Majer, K. (2023). Semi–supervised vs. supervised learning for mental health monitoring: A case study on bipolar disorder, International Journal of Applied Mathematics and Computer Science 33(3): 419–428, DOI: 10.34768/amcs-2023-0030.
]Search in Google Scholar
[
Chouhan, V., Singh, S.K., Khamparia, A., Gupta, D., Tiwari, P., Moreira, C. and De Albuquerque, V.H.C. (2020). A novel transfer learning based approach for pneumonia detection in chest X-ray images, Applied Sciences 10(2): 559.
]Search in Google Scholar
[
Chowdhury, M.E., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M.A., Mahbub, Z.B. and Islam, M.T. (2020). Can ai help in screening viral and covid-19 pneumonia?, IEEE Access 8: 132665–132676.
]Search in Google Scholar
[
Cichosz, P. (2023). Bag of words and embedding text representation methods for medical article classification, International Journal of Applied Mathematics and Computer Science 33(4): 603–621, DOI: 10.34768/amcs-2023-0043.
]Search in Google Scholar
[
Dhere, A. and Sivaswamy, J. (2022). Covid detection from chest X-ray images using multi-scale attention, IEEE Journal of Biomedical and Health Informatics 26(4): 1496–1505.
]Search in Google Scholar
[
El-Douh, A.A.R., Lu, S.F., Elkouny, A.A. and Amein, A.S. (2022). Hybrid cryptography with a one–time stamp to secure contact tracing for covid–19 infection, International Journal of Applied Mathematics and Computer Science 32(1): 139–146, DOI: 10.34768/amcs-2022-0011.
]Search in Google Scholar
[
Feng, Y., Xu, X., Wang, Y., Lei, X., Teo, S.K., Sim, J.Z.T. and Tan, C.H. (2021). Deep supervised domain adaptation for pneumonia diagnosis from chest X-ray images, IEEE Journal of Biomedical and Health Informatics 26(3): 1080–1090.
]Search in Google Scholar
[
Feng, Y., Yang, X., Qiu, D., Zhang, H., Wei, D. and Liu, J. (2022). PCXRNet: Pneumonia diagnosis from chest X-ray images using condense attention block and multiconvolution attention block, IEEE Journal of Biomedical and Health Informatics 26(4): 1484–1495.
]Search in Google Scholar
[
Fu, Y., Xue, P., Zhang, Z. and Dong, E. (2023). Pka 2-net: Prior knowledge-based active attention network for accurate pneumonia diagnosis on chest X-ray images, IEEE Journal of Biomedical and Health Informatics 27(7): 3513–3524.
]Search in Google Scholar
[
Hashmi, M.F., Katiyar, S., Keskar, A.G., Bokde, N.D. and Geem, Z.W. (2020). Efficient pneumonia detection in chest X-ray images using deep transfer learning, Diagnostics 10(6): 417.
]Search in Google Scholar
[
Huang, G., Liu, Z., Van Der Maaten, L. and Weinberger, K.Q. (2017). Densely connected convolutional networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp. 4700–4708.
]Search in Google Scholar
[
Hussain, A., Amin, S.U., Lee, H., Khan, A., Khan, N.F. and Seo, S. (2023). An automated chest X-ray image analysis for covid-19 and pneumonia diagnosis using deep ensemble strategy, IEEE Access 11: 97207–97220.
]Search in Google Scholar
[
Jain, R., Nagrath, P., Kataria, G., Kaushik, V.S. and Hemanth, D.J. (2020a). Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning, Measurement 165: 108046.
]Search in Google Scholar
[
Jaiswal, A.K., Tiwari, P., Kumar, S., Gupta, D., Khanna, A. and Rodrigues, J.J. (2019). Identifying pneumonia in chest X-rays: A deep learning approach, Measurement 145: 511–518.
]Search in Google Scholar
[
Khan, W., Zaki, N. and Ali, L. (2021). Intelligent pneumonia identification from chest X-rays: A systematic literature review, IEEE Access 9: 51747–51771.
]Search in Google Scholar
[
Kowal, M., Skobel, M., Gramacki, A. and Korbicz, J. (2021). Breast cancer nuclei segmentation and classification based on a deep learning approach, International Journal of Applied Mathematics and Computer Science 31(1): 85–106.
]Search in Google Scholar
[
Kundu, R., Das, R., Geem, Z.W., Han, G.T. and Sarkar, R. (2021). Pneumonia detection in chest X-ray images using an ensemble of deep learning models, PLoS ONE 16(9): e0256630.
]Search in Google Scholar
[
Li, X., Shen, L., Xie, X., Huang, S., Xie, Z., Hong, X. and Yu, J. (2020). Multi-resolution convolutional networks for chest X-ray radiograph based lung nodule detection, Artificial Intelligence in Medicine 103: 101744.
]Search in Google Scholar
[
Liang, G. and Zheng, L. (2020a). A transfer learning method with DEEP residual network for pediatric pneumonia diagnosis, Computer Methods and Programs in Biomedicine 187: 104964.
]Search in Google Scholar
[
Liang, G. and Zheng, L. (2020b). A transfer learning method with deep residual network for pediatric pneumonia diagnosis, Computer Methods and Programs in Biomedicine 187: 104964.
]Search in Google Scholar
[
Ling, G. and Cao, C. (2019). Automatic detection and diagnosis of severe viral pneumonia CT images based on LDA-SVM, IEEE Sensors Journal 20(20): 11927–11934.
]Search in Google Scholar
[
Malla, P.P., Sahu, S. and Alutaibi, A.I. (2023). Classification of tumor in brain MR images using deep convolutional neural network and global average pooling, Processes 11(3): 679.
]Search in Google Scholar
[
Mooney, P. (2018). Chest X-ray images (pneumonia), https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia.
]Search in Google Scholar
[
Muhammad, Y., Alshehri, M.D., Alenazy,W.M., Vinh Hoang, T. and Alturki, R. (2021). Identification of pneumonia disease applying an intelligent computational framework based on deep learning and machine learning techniques, Mobile Information Systems 2021(1): 9989237.
]Search in Google Scholar
[
Pasa, F., Golkov, V., Pfeiffer, F., Cremers, D. and Pfeiffer, D. (2019). Efficient deep network architectures for fast chest X-ray tuberculosis screening and visualization, Scientific Reports 9(1): 1–9.
]Search in Google Scholar
[
Rahman, T., Chowdhury, M.E., Khandakar, A., Islam, K.R., Islam, K.F., Mahbub, Z.B. and Kashem, S. (2020a). Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray, Applied Sciences 10(9): 3233.
]Search in Google Scholar
[
Rahman, T., Chowdhury, M.E., Khandakar, A., Islam, K.R., Islam, K.F., Mahbub, Z.B. and Kashem, S. (2020b). Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray, Applied Sciences 10(9): 3233.
]Search in Google Scholar
[
Rak, E., Szczur, A., Bazan, J.G. and Bazan-Socha, S. (2023). Assessment measures of an ensemble classifier based on the distributivity equation to predict the presence of severe coronary artery disease, International Journal of Applied Mathematics and Computer Science 33(3): 361–377, DOI: 10.34768/amcs-2023-0026.
]Search in Google Scholar
[
Sheu, R.K., Pardeshi, M.S., Pai, K.C., Chen, L.C., Wu, C.L. and Chen, W.C. (2023). Interpretable classification of pneumonia infection using explainable AI (XAI-ICP), IEEE Access 11: 28896–28919.
]Search in Google Scholar
[
Siddiqi, R. (2019). Automated pneumonia diagnosis using a customized sequential convolutional neural network, Proceedings of the 2019 3rd International Conference on Deep Learning Technologies, New York, USA, pp. 64–70.
]Search in Google Scholar
[
Singh, S. and Tripathi, B.K. (2022). Pneumonia classification using quaternion deep learning, Multimedia Tools and Applications 81(2): 1743–1764.
]Search in Google Scholar
[
Sirazitdinov, I., Kholiavchenko, M., Mustafaev, T., Yixuan, Y., Kuleev, R. and Ibragimov, B. (2019). Deep neural network ensemble for pneumonia localization from a large-scale chest X-ray database, Computers & Electrical Engineering 78: 388–399.
]Search in Google Scholar
[
Souza, J.C., Diniz, J.O.B., Ferreira, J.L., da Silva, G.L.F., Silva, A.C. and de Paiva, A.C. (2019). An automatic method for lung segmentation and reconstruction in chest x-ray using deep neural networks, Computer Methods and Programs in Biomedicine 177: 285–296.
]Search in Google Scholar
[
Stephen, O., Sain, M., Maduh, U.J. and Jeong, D.U. (2019). An efficient deep learning approach to pneumonia classification in healthcare, Journal of Healthcare Engineering 2019(1):4180949..
]Search in Google Scholar
[
Szepesi, P. and Szilágyi, L. (2022). Detection of pneumonia using convolutional neural networks and deep learning, Biocybernetics and Biomedical Engineering 42(3): 1012–1022.
]Search in Google Scholar
[
Taylor, A.G., Mielke, C. and Mongan, J. (2018). Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: A retrospective study, PLoS Medicine 15(11): e1002697.
]Search in Google Scholar
[
Toğaçar, M., Ergen, B., Cömert, Z. and Özyurt, F. (2020). A deep feature learning model for pneumonia detection applying a combination of MRMR feature selection and machine learning models, IRBM 41(4): 212–222.
]Search in Google Scholar
[
Wang, H., Shen, Y., Wang, S., Xiao, T., Deng, L., Wang, X. and Zhao, X. (2019). Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer’s disease, Neurocomputing 333: 145–156.
]Search in Google Scholar
[
Wózniak,M., Połap, D., Capizzi, G., Sciuto, G.L., Kósmider, L. and Frankiewicz, K. (2018). Small lung nodules detection based on local variance analysis and probabilistic neural network, Computer Methods and Programs in Biomedicine 161: 173–180.
]Search in Google Scholar
[
Yang, Z.Y. and Zhao, Q. (2020). A multiple deep learner approach for X-ray image-based pneumonia detection, 2020 International Conference on Machine Learning and Cybernetics (ICMLC), Adelaide, Australia, pp. 70–75.
]Search in Google Scholar
[
Yaseliani, M., Hamadani, A.Z., Maghsoodi, A.I. and Mosavi, A. (2022). Pneumonia detection proposing a hybrid deep convolutional neural network based on two parallel visual geometry group architectures and machine learning classifiers, IEEE Access 10: 62110–62128.
]Search in Google Scholar
[
Zhang, J., Xie, Y., Pang, G., Liao, Z., Verjans, J., Li, W. and Xia, Y. (2020). Viral pneumonia screening on chest X-rays using confidence-aware anomaly detection, IEEE Transactions on Medical Imaging 40(3): 879–890.
]Search in Google Scholar
[
Zhang, X., Han, L., Sobeih, T., Han, L., Dempsey, N., Lechareas, S. and Zhang, D. (2022). CXR-Net: a multitask deep learning network for explainable and accurate diagnosis of COVID-19 pneumonia from chest X-ray images, IEEE Journal of Biomedical and Health Informatics 27(2): 980–991.
]Search in Google Scholar