A DEEP CONVOLUTIONAL NEURAL NETWORK-BASED MOBILE APPLICATION FOR EARLY AUTO-DETECTION OF PNEUMONIA FROM CHEST X-RAY IMAGES

Abiyev, R.H. and Ma’aitah, M.K.S. (2018). Deep convolutional neural networks for chest diseases detection, Journal of Healthcare Engineering, 2018, 1–11.

Almaslukh, B. (2021). A lightweight deep learning-based pneumonia detection approach for energy-efficient medical systems, Wireless Communications and Mobile Computing, 2021(1), 1-14.

Beletew, B., Minerew, M., Mengesha, A., Wudu, M. and Azmeraw, M. (2020). Prevalence of pneumonia and its associated factors among under-five children in East Africa: a systematic review and meta-analysis, BMC Pediatrics, 20(254), 1-13.

El Asnaoul, K. (2021). Design ensemble deep learning model for pneumonia disease classification”, International Journal of Multimedia Information Retrieval, 10(1), 55-68.

Gereige, R.S. and Laufer, P.M. (2013). Pneumonia, Pediatrics in Review, 34(10), 438-456.

Hashmi, M.F., Katiyar, S. Hashmi, A.A.W. and Keskar, N.D. (2021). Pneumonia detection in chest x-ray images using compound scaled deep learning model, Automatika Journal for Control, Measurement, Electronics, Computing and Communications, 62(3-4), 397-406.

Hashmi, M.F., Katiyar, S..Keskar, A., Bokdeand, N.D. and Geem, Z.W. (2020). Efficient pneumonia detection in chest X-ray images using deep transfer learning, Diagnistics, 10, 1-23.

He, K.., Zhang, X., Ren, S. and Sun, J. (2016). Deep Residual Learning for Image Recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 27-30 June, Las Vegas, NV, USA, 770-778.

Howard, A.G., Zhu, M. Chen, B. Kalenichenko, D., Wang, W., Weyand, T., Andreettoand, M. and Adam, H. (2017). MobileNets: efficient convolutional neural networks for mobile vision applications, computer vision and pattern recognition, 1-9.

James, P.B., Wardle, J., Steel, A. and Adams, J. (2018). Traditional, complementary and alternative medicine use in Sub-Sahara Africa: a systematic review, BMJ Global Health, 3, 1-18.

Kassylkassova, K., Omarov, B., Kazbekova, G., Kozhamkulova, Z., Maikotovand, M. and Bidakhmet, Z. (2023). Automated pneumonia diagnosis using a 2D deep convolutional neural network with chest X-ray images, International Journal of Advanced Computer Science and Applications, 14(2), 699-708.

Kiconco, G., Turyasiima, M., Ndamira, A., Yamile, O.A., Egesa, W.I., Ndiwimana M. and Maren, M.B. (2021). Prevalence and associated factors of pneumonia among under-fives with acute respiratory symptoms: a cross sectional study at a Teaching Hospital in Bushenyi district, Western Uganda, African Health Sciences, 21( 4), 1701-1710.

Kifle, M., Yadeta, T.A., Debella, A., and Mussa, I. (2023). Determinants of pneumonia among under-five children at HiwotFana specialized hospital, Eastern Ethiopia: unmatched case-control study, BMC Pulmonary Medicine, 23, 1-11.

Le Roux, D.M., Myer, L., Nicol, M.P. and Zar, H.J. (2015). Incidence of childhood pneumonia: facility-based surveillance estimate compared to measured incidence in a South African birth cohort study”, BMJ Open, 5, 1-6.

Liu, L., Oza, S., Hogan, D., Chu, Y., Perin, J., Zhu, J., Lawn, J.E., Cousens, S., Mathers, C. and Black, R.E. (2016). Global, region, and national causes of under-5 mortality in 2000-15: an updated systematic analysis with implications for sustainable Development Goals”, The Lancet, 388(10063), 3027-3035.

Liu, Q. Liu, Z., Yong, S., Jiaand, K. and Razmjooy, N. (2020). Computer-aided breast cancer diagnosis based on image segmentation and interval analysis, Journal for Control, Measurement, Electronics, Computing and Communications, 61(3), 496-506.

Ma, B., Li, X., Xia, Y. and Zhang, Y. (2020). Autonomous deep learning: A genetic DCNN designer for image classification, Neurocomputing, 379(28), 152-161.

Mabrouk, A., Diaz Redondo, R.P., Dahou, R.P. AbdElazizand M. Kayed, M. (2022). Pneumonia detection on chest X-ray images using ensemble of deep conventional neural networks, Applied Sciences, 12, 1-15.

Mohammed, F.A., Tune, K.K., Assefa, B.G., Jett, M. and Muhie, S. (2024). Medical Image Classifications Using Convolutional Neural Networks: A survey of current methods and statistical modeling of the literature, Machine Learning and Knowledge Extraction, 6(1), 699-735.

Munna, M.S., Chowdhury, R. and Siddiquee, A.M. (2024). A comparative study of YOLO models for pneumonia detection, International Journal for Multidisciplinary Research, 6(3), 1-10.

Popoola, J.J. (2018). Automatic Recognition of both Inter and Intra Classes of Digital Modulated Signals using Artificial Neural Network, Journal of Engineering Science and Technology, 9(2), 273-285.

Quinton, L.J., Walkey, A.J. and Mizgerd, J.P. (2018). Integrative physiology of pneumonia, Physiological Reviews, 98(3), 1417-1464.

Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Ball, R.L., Langlotz, C. Shpanskaya, K., Lungre, M.P. and Ng, A.Y. (2017). CheXNet: radiologist-level pneumonia detection on chest x-rays with deep learning, ArXiv, abs/1711.05225, 1-7.

Sabottke, C.F. and Spieler, B.M. (2020). The effect of image resolution on deep learning in radiography, Radiologist Artificial Intelligence, 2(1), 1-7.

Saraiva, A.A., Santos, D.B.S., Nator, J.C., Jose, V.M.S., Ferreira, F.N.M., Antonio, V. and Salviano, S. (2019). Models of learning to classify x-ray images for the detection of pneumonia using neural networks, In proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies, Prague, Czech Republic, February 22-24,76-88.

Seramo, R.K., Awol, S.M., Wabe, Y.A., and Ali, M.M. (2022). Determinants of pneumonia among children attending public health in Worabetpwn, Scientific Reports, 12(1), 1-9.

Siddiqqi R. and Javaid, S. (2024). Deep learning for pneumonia detection in chest X-rays Images: a comprehensive survey, Journal of Imaging, 10(176), 1-35.

Solomon, Y., Kofole, Z., Fantaye, T., and Ejigu, S. (2021) Prevalence of pneumonia and its determinant factors among under-five children in Gamo Zone, Southern Ethiopia, Frontiers in Pediatrics, 1-8.

Stephen, O., Sain, M., Maduhand, U.J. and .Jeong, D-U. (2019). An efficient deep learning approach to pneumonia classification in healthcare, Journal of Healthcare Engineering, 1-7.

Tawsifur, R., Muhammad, E.H.C., Amith, K., Khandaker, R.I., Khandaker, F.I., Zaid, B.M., Muhammad, A.K. and Saad, K. (2020). Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray, Applied Sciences, 10(9), 1-17.

Togacar, M., Ergen, B. and Comert,. (2019). A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models, IRBM, 1-11.

Torres, A., Cilloniz, C., Niederman, M.S., Menendez, R., Chalmers, J.D., Wunderink, R.G. and van der Poll, T. (2021). Pneumonia, Nature Reviews Disease Primers, 7(25), 1-28.

Wondi, C., Obi-Jeff, C., Falade, A., Watkins, K.., and Omokore, O.A. (2020). Pneumonia in Nigeria: the way forward”, Pediatrics Pulmonology, 55, S5-S9.

Yaguo-Ide, L.E. and Nte, A. (2011). Childhood Pneumonia and under-five morbidity and mortality at the University of Port Harcourt Teaching Hospital- a situational analysis, The Nigerian Health Journal, 11(3), 93-96.