Machine learning in lung lesion detection caused by certain diseases

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dc.citation.epage1092
dc.citation.issue4
dc.citation.journalTitleМатематичне моделювання та комп'ютинг
dc.citation.spage1084
dc.contributor.affiliationНаціональний університет “Львівська політехніка”
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorХорощук, Д.
dc.contributor.authorЛюбінський, Б. Б.
dc.contributor.authorKhoroshchuk, D.
dc.contributor.authorLiubinskyi, B. B.
dc.coverage.placenameЛьвів
dc.date.accessioned2025-03-10T09:22:04Z
dc.date.created2023-02-28
dc.date.issued2023-02-28
dc.description.abstractСтаття присвячена використанню нейронних мереж для аналізу медичних зображень, а саме: X-променевих зображень. Здійснено огляд нейронних мереж, які використовуються для аналізу медичних зображень. Така нейромережа була реалізована та протестована на сторонніх зображеннях.
dc.description.abstractThe work highlights neural network applications to medical images, namely X-ray images. An overview of neural networks used to analyze medical images was conducted. Such a neural network has been implemented and tested on third-party images.
dc.format.extent1084-1092
dc.format.pages9
dc.identifier.citationKhoroshchuk D. Machine learning in lung lesion detection caused by certain diseases / D. Khoroshchuk, B. B. Liubinskyi // Mathematical Modeling and Computing. — Lviv Politechnic Publishing House, 2023. — Vol 10. — No 4. — P. 1084–1092.
dc.identifier.citationenKhoroshchuk D. Machine learning in lung lesion detection caused by certain diseases / D. Khoroshchuk, B. B. Liubinskyi // Mathematical Modeling and Computing. — Lviv Politechnic Publishing House, 2023. — Vol 10. — No 4. — P. 1084–1092.
dc.identifier.doidoi.org/10.23939/mmc2023.04.1084
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/64089
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofМатематичне моделювання та комп'ютинг, 4 (10), 2023
dc.relation.ispartofMathematical Modeling and Computing, 4 (10), 2023
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dc.relation.references[19] Hacking C., Luong D. Pneumonia. https://radiopaedia.org/articles/pneumonia.
dc.relation.referencesen[1] Chemlal Y., Azouazi M. Implementing quality assurance practices in teaching machine learning in higher education. Mathematical Modeling and Computing. 10 (3), 660–667 (2023).
dc.relation.referencesen[2] Bellaj K., Benmir M., Boujen S. Enhancing image inpainting through image decomposition and deep neural networks. Mathematical Modeling and Computing. 10 (3), 720–732 (2023).
dc.relation.referencesen[3] Krizhevsky A., Sutskever I., Hinton G. E. ImageNet classification with deep convolutional neural networks. Communications of the ACM. 60 (6), 84–90 (2017).
dc.relation.referencesen[4] Liu P., Lu L., Zhang J., Huo T., Liu S., Ye Z. Application of Artificial Intelligence in Medicine: An Overview. Current Medical Science. 41 (60), 1105–1115 (2021).
dc.relation.referencesen[5] COVID-19 Data Explorer – Our World in Data. https://ourworldindata.org/explorers/coronavirus-dataexplorer.
dc.relation.referencesen[6] On the approval of clinical protocols for the provision of medical care in the specialty "Pulmonology": Order of the Ministry of Health of Ukraine from 19.03.2007 No 128. https://zakon.rada.gov.ua/rada/show/v0128282-07.
dc.relation.referencesen[7] Bursov A. I. Application of artificial intelligence in medical data analysis. Almanac of Clinical Medicine. 47 (7), 630–633 (2019).
dc.relation.referencesen[8] Van Ginneken B. Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning. Radiological Physics and Technology. 10 (1), 23–32 (2017).
dc.relation.referencesen[9] Yadav S. Brief Intro to Medical Image Analysis and Deep Learning. https://medium.com/@saurabh.yadav919/brief-intro-of-medical-image-analysis-and-deep-learning810df940d2f7.
dc.relation.referencesen[10] Topol E. J. High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine. 25 (1), 44–56 (2019).
dc.relation.referencesen[11] Elgendi M., Nasir M. U., Tang Q., Fletcher R. R., Howard N., Menon C., Ward R., Parker W., Nicolaou S. The Performance of Deep Neural Networks in Differentiating Chest X-Rays of COVID-19 Patients From Other Bacterial and Viral Pneumonias. Frontiers in Medicine. 7, 550 (2020).
dc.relation.referencesen[12] Almezhghwi K., Serte S., Al-Turjman F. Convolutional neural networks for the classification of chest X-rays in the IoT era. Multimedia Tools and Applications. 80 (19), 29051–29065 (2021).
dc.relation.referencesen[13] Tang Y.-X., Tang Y.-B., Peng Y., Yan K., Bagheri M., Redd B. A., Brandon C. J., Lu Z., Han M., Xiao J., Summers R. M. Automated abnormality classification of chest radiographs using deep convolutional neural networks. npj Digital Medicine. 3 (1), 70 (2020).
dc.relation.referencesen[14] Pasa F., Golkov V., Pfeiffer F., Cremers D., Pfeiffer D. Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization. Scientific Reports. 9 (1), 6268 (2019).
dc.relation.referencesen[15] Maity A., Nair R. T., Chandra A. Image Pre-processing techniques comparison: COVID-19 detection through Chest X-Rays via Deep Learning. International Journal of Scientific Research in Science and Technology. 7 (6), 113–123 (2020).
dc.relation.referencesen[16] Patel O., Maravi P. S. Y., Sharma S. A Comparative Study of Histogram Equalization Based Image Enhancement Techniques for Brightness Preservation and Contrast Enhancement. Signal & Image Processing: An International Journal. 4 (5), 11–25 (2013).
dc.relation.referencesen[17] Gieiczyk A., Marciniak A., Tarczewska M., Lutowski Z. Pre-processing methods in chest X-ray image classification. PLOS ONE. 17 (4), e0265949 (2022).
dc.relation.referencesen[18] Chest X-Ray Images (Pneumonia). https://www.kaggle.com/datasets/paultimothymooney/chest-xraypneumonia.
dc.relation.referencesen[19] Hacking C., Luong D. Pneumonia. https://radiopaedia.org/articles/pneumonia.
dc.relation.urihttps://ourworldindata.org/explorers/coronavirus-dataexplorer
dc.relation.urihttps://zakon.rada.gov.ua/rada/show/v0128282-07
dc.relation.urihttps://medium.com/@saurabh.yadav919/brief-intro-of-medical-image-analysis-and-deep-learning810df940d2f7
dc.relation.urihttps://www.kaggle.com/datasets/paultimothymooney/chest-xraypneumonia
dc.relation.urihttps://radiopaedia.org/articles/pneumonia
dc.rights.holder© Національний університет “Львівська політехніка”, 2023
dc.subjectкласифікація зображень
dc.subjectзгорткові нейронні мережі
dc.subjectглибоке навчання
dc.subjectX-променева радіографія грудної клітки
dc.subjectimage classification
dc.subjectconvolutional neural networks
dc.subjectdeep learning
dc.subjectchest Xray radiography
dc.titleMachine learning in lung lesion detection caused by certain diseases
dc.title.alternativeМашинне навчання для виявлення уражень легень, які спричинені певними захворюваннями
dc.typeArticle

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Mathematical Modeling And Computing. – 2023. – Vol. 10, No. 4