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الإتاحة: https://doi.org/10.3390/molecules28041821Test
https://hdl.handle.net/11323/10383Test
https://repositorio.cuc.edu.coTest/

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النص الكامل

4

دورية أكاديمية

Factores institucionales-pedagógicos, sociodemográficos y contextuales relacionados con el rendimiento académico universitario: un análisis multinivel ; Factors related to academic performance in higher education: a multilevel approach

المؤلفون: Gutiérrez-Monsalve, Jaime A., López-Velásquez, John F., Castillo Grisales , Julián Andrés, Segura-Cardona, Angela M.

المصدر: https://revistascientificas.cuc.edu.co/culturaeducacionysociedad/article/view/4663Test.

مصطلحات موضوعية: Rendimiento académico, factores sociodemográficos, factores institucionales, factores contextuales, calidad de la educación, análisis multinivel, Academic performance, sociodemographic factors, institutional factors, contextual factors, quality of education, multilevel analysis

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الإتاحة: https://doi.org/10.17981/cultedusoc.15.1.2024.4663Test
https://hdl.handle.net/11323/11406Test

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5

دورية أكاديمية

Optimal synergic deep learning for COVID-19 classification using chest x-ray images

المؤلفون: Escorcia-Gutierrez, José, Gamarra, Margarita, Soto-Diaz, Roosvel, alsafari, safa, Yafoz, Ayman, Mansour, Romany F.

المصدر: https://www.techscience.com/cmc/v75n3/52554Test.

مصطلحات موضوعية: Artificial intelligence, Chest X-ray, COVID-19, Optimized synergic deep learning, Preprocessing, Public health

وصف الملف: 16 páginas; application/pdf

العلاقة: Computers, Materials and Continua; [1] S. Sadhana, S. Pandiarajan, E. Sivaraman and D. Daniel, “AI-based power screening solution for SARSCOV2 infection: A sociodemographic survey and COVID-19 cough detector,”Procedia Computer Science, vol. 194, no. 9, pp. 255–271, 2021.; [2] E. Mahase, “Coronavirus: COVID-19 has killed more people than SARS and MERS combined, despite lower case fatality rate,” BMJ, vol. 368, pp. m641, 2020.; [3] U. Rani and R. K. Dhir, “Platform work and the COVID-19 pandemic,” The Indian Journal of Labour Economics, vol. 63, no. S1, pp. 163–171, 2020.; [4] M. Ahmadi, A. Sharifi, S. Dorosti, S. J. Ghoushchi and N. Ghanbari, “Investigation of effective climatology parameters on COVID-19 outbreak in Iran,” Science of the Total Environment, vol. 729, no. 8, pp. 138705, 2020.; [5] Y. Fang, H. Zhang, J. Xie, M. Lin, L. Ying et al., “Sensitivity of chest CT for COVID-19: Comparison to RT-PCR,” Radiology, vol. 296, no. 2, pp. E115–E117, 2020.; [6] M. N. Ikeda, K. 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Panwar, P. K. Gupta, M. K. Siddiqui, R. M. Menendez, V. Singh et al., “Application of deep learning for fast detection of COVID-19 in X-rays using nCOVnet,” Chaos, Solitons & Fractals, vol. 128, no. 3, pp. 109944, 2020.; [22] S. Toraman, T. B. Alakus and I. Turkoglu, “Convolutional CapsNet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks,” Chaos, Solitons & Fractals, vol. 140, no. 18, pp. 110122, 2020.; [23] M. Nour, Z. Cömert and K. Polat, “A novel medical diagnosis model for COVID-19 infection detection based on deep features and Bayesian optimization,”Applied Soft Computing, vol. 97, no. Part A, pp. 106580, 2020.; [24] R. F. Mansour, J. Escorcia-Gutierrez, M. Gamarra, D. Gupta, O. Castillo et al., “Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification,” Pattern Recognition Letters, vol. 151, no. 151, pp. 267–274, 2021.; [25] S. Ahuja, B. K. Panigrahi, N. Dey, V. 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Yafoz et al., "Optimal synergic deep learning for covid-19 classification using chest x-ray images," Computers, Materials & Continua, vol. 75, no.3, pp. 5255–5270, 2023. https://doi.org/10.32604/cmc.2023.033731Test; https://hdl.handle.net/11323/10601Test; Corporación Universidad de la Costa; REDICUC - Repositorio CUC; https://repositorio.cuc.edu.coTest/

الإتاحة: https://doi.org/10.32604/cmc.2023.033731Test
https://doi.org/10.1007/s12652-018-0933-xTest
https://doi.org/10.1007/s00779-020-01492-2Test
https://hdl.handle.net/11323/10601Test
https://repositorio.cuc.edu.coTest/

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6

دورية أكاديمية

A system of additive functional equations in complex Banach algebras

المؤلفون: Paokanta, Siriluk, Dehghanian, Mehdi, Park, Choonkil, Sayyari, Yamin

المصدر: https://www.degruyter.com/document/doi/10.1515/dema-2022-0165/htmlTest.

مصطلحات موضوعية: Additive mapping, F-hom-der, Fixed point method, Hyers-Ulam stability, System of additive functional equations

وصف الملف: 10 páginas; application/pdf

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Tamilvanan, G. Balasubramanian, B. Noori, and A. Najati, On a functional equation that has the quadraticmultiplicative property, Open Math. 18 (2020), 837–845, DOI: https://doi.org/10.1515/math-2020-0032Test.; [13] A. Thanyacharoen and W. Sintunavarat, The new investigation of the stability of mixed type additive-quartic functional equations in non-Archimdean spaces, Demonstr. Math. 53 (2020), 174–192, DOI: https://doi.org/10.1515/dema2020-0009Test.; [14] A. Thanyacharoen and W. Sintunavarat, On new stability results for composite functional equations in quasi-β-normed spaces, Demonstr. Math. 54 (2021), 68–84, DOI: https://doi.org/10.1515/dema-2021-0002Test.; [15] Y. Sayyari, M. Dehghanian, C. Park, and J. Lee, Stability of hyper homomorphisms and hyper derivations in complex Banach algebras, AIMS Math. 7 (2022), no. 6, 10700–10710, DOI: https://doi.org/10.3934/math.2022597Test.; [16] I. Hwang and C. Park, Bihom derivations in Banach algebras, J. 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دورية أكاديمية

An artificial bee bare-bone hunger games search for global optimization and high-dimensional feature selection

المؤلفون: Chen, Zhiqing, Xuan, Ping, Asghar Heidari, Ali, Liu, Lei, Wu, Chengwen, Chen, Huiling, Escorcia-Gutierrez, José, Mansour, Romany F.

المصدر: https://www.sciencedirect.com/science/article/pii/S2589004223007563?via%3DihubTest.

مصطلحات موضوعية: Genetics, Computational bioinformatics, Algorithms

وصف الملف: 39 páginas; application/pdf

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الإتاحة: https://doi.org/10.1016/j.isci.2023.106679Test
https://doi.org/10.1109/TCSVT.2022.3227348Test
https://hdl.handle.net/11323/10499Test
https://repositorio.cuc.edu.coTest/

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النص الكامل

8

دورية أكاديمية

Community preferences for participating in ecotourism: a case study in a coastal lagoon in Colombia

المؤلفون: Pineda, F., Padilla, J., Granobles-Torres, J.C., Echeverri-Rubio, A., Botero, C.M., Suarez, A.

المصدر: https://www.sciencedirect.com/science/article/pii/S2667010023000355Test.

مصطلحات موضوعية: Coastal ecosystem, Community participation, Local perceptions, Swamp mallorquín, Willingness to participate

جغرافية الموضوع: Colombia

وصف الملف: 8 páginas; application/pdf

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الإتاحة: https://doi.org/10.1016/j.envc.2023.100713Test
https://doi.org/10.1016/j.jort.2022.100523Test
https://doi.org/10.4135/9781412957397.n43Test
https://doi.org/10.1080/10871200802187097Test
https://hdl.handle.net/11323/10480Test
https://repositorio.cuc.edu.coTest/

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النص الكامل

9

دورية أكاديمية

Biomarkers for dementia in Latin American countries: gaps and opportunities

المؤلفون: Parra, Mario A., Orellana, Paulina, Leon, Tomas, Cabello G., Victoria, Henriquez, Fernando, Gómez, Rodrigo, Avalos, Constanza, Damian, Andrés, Slachevsky, Andrea, Ibáñez, Agustín, Zetterberg, Henrik, Tijms, Betty M., Yokoyama, Jennifer S., Piña-Escudero, Stefanie D., Nicholas Cochran, J., Matallana, Diana L., Acosta, Daisy, Allegri, Ricardo Francisco, Arias-Suárez, Bianca P., Barra, Bernardo, Behrens, Maria Isabel, Brucki, Sonia M. D., Busatto, Geraldo, Caramelli, Paulo, Castro-Suarez, Sheila, Contreras, Valeria, Custodio, Nilton, Dansilio, Sergio, De la Cruz-Puebla, Myriam, Cruz de Souza, Leonardo, Diaz, Monica M., Duque, Lissette, Farías, Gonzalo A., Ferreira, Sergio T., Magrath Guimet, Nahuel, Kmaid, Ana, Lira, David, Lopera, Francisco, Mar Meza, Beatriz, Miotto, Eliane C., Nitrini, Ricardo, Núñez, Alberto, O’Neill, Santiago, Ochoa, John, Pintado-Caipa, Maritza, França Resende, Elisa de Paula, Risacher, Shannon, Rojas, Luz Angela, Sabaj, Valentina, Schilling, Lucas, Sellek, Allis F., Sosa, Ana, Takada, Leonel T., Teixeira, Antonio L., Unaucho-Pilalumbo, Martha, Duran-Aniotz, Claudia

المصدر: https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/alz.12757Test.

مصطلحات موضوعية: Dementia, Latin American, Biomarkers

جغرافية الموضوع: Latin America

وصف الملف: 15 páginas; application/pdf

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Prevalence of dementia in Latin America: a collaborative study of population-based cohorts. Int Psychogeriatr. 2009;21:622-630.; 8. Wolters FJ, Chibnik LB, Waziry R, et al. Twenty-seven-year time trends in dementia incidence in Europe and the United States: the Alzheimer Cohorts Consortium. Neurology. 2020;95:e519-e531.; 9. Prince MJ, Wimo A, Guerchet MM, Ali GC, Wu Y-T, Prina M. World alzheimer report 2015 – The global impact of dementia: an analysis of prevalence, incidence, costs and trends. London: Alzheimer’s Disease International, 2015. 84 p.; 11. Jack CR, Jr, Bennett DA, Blennow K, et al. NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 2018;14:535-562.; 12. Sperling RA, Aisen PS, Beckett LA, et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7:280- 292.; 13. Dubois B, Feldman HH, Jacova C, et al. Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria. Lancet Neurol. 2007;6:734-746.; 14. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer’s disease: report of the NINCDSADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology. 1984;34:939-944.; 15. El Kadmiri N, Said N, Slassi I, El Moutawakil B, Nadifi S. Biomarkers for Alzheimer disease: classical and novel candidates’ review. Neuroscience. 2018;370:181-190.; 16. Blennow K, Hampel H. CSF markers for incipient Alzheimer’s disease. Lancet Neurol. 2003;2:605-613.; 17. Burger nee Buch K, Padberg F, Nolde T, et al. Cerebrospinal fluid tau protein shows a better discrimination in young old (; 18. Hampel H, Buerger K, Zinkowski R, et al. Measurement of phosphorylated tau epitopes in the differential diagnosis of Alzheimer disease: a comparative cerebrospinal fluid study. Arch Gen Psychiatry. 2004;61:95-102.; 19. Hampel H, Burger K, Teipel SJ, Bokde AL, Zetterberg H, Blennow K. Core candidate neurochemical and imaging biomarkers of Alzheimer’s disease. Alzheimers Dement. 2008;4:38-48.; 20. Mattsson N, Zetterberg H, Hansson O, et al. CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. JAMA. 2009;302:385-393.; 21. Bobinski M, de Leon MJ, Wegiel J, et al. The histological validation of post mortem magnetic resonance imaging-determined hippocampal volume in Alzheimer’s disease. Neuroscience. 2000;95:721- 725.; 22. Zarow C, Vinters HV, EllisWG, et al. Correlates of hippocampal neuron number in Alzheimer’s disease and ischemic vascular dementia. Ann Neurol. 2005;57:896-903.; 23. Silverman DH, Small GW, Chang CY, et al. 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Aschenbrenner AJ, Gordon BA, Benzinger TLS, Morris JC, Hassenstab JJ. Influence of tau PET, amyloid PET, and hippocampal volume on cognition in Alzheimer disease. Neurology. 2018;91:e859-e866.; 29. Jack CR, Jr, Bennett DA, Blennow K, et al. A/T/N: an unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology. 2016;87:539-547.; 30. Irizarry MC. Biomarkers of Alzheimer disease in plasma. NeuroRx. 2004;1:226-234.; 31. Thambisetty M, Lovestone S. Blood-based biomarkers of Alzheimer’s disease: challenging but feasible. Biomark Med. 2010;4:65-79.; 32. Lista S, Faltraco F, Prvulovic D, Hampel H. Blood and plasma-based proteomic biomarker research in Alzheimer’s disease. Prog Neurobiol. 2013;101-102:1-17.; 33. Zetterberg H, Schott JM. Blood biomarkers for Alzheimer’s disease and related disorders. Acta Neurol Scand. 2022;146(1):51- 55.; 34. Zetterberg H, Blennow K. Moving fluid biomarkers for Alzheimer’s disease from research tools to routine clinical diagnostics. 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Nature. 2006;442:916-919.; 41. Poorkaj P, Bird TD, Wijsman E, et al. Tau is a candidate gene for chromosome 17 frontotemporal dementia. Ann Neurol. 1998;43:815-825.; 42. Saunders AM, Strittmatter WJ, Schmechel D, et al. Association of apolipoprotein E allele epsilon 4 with late-onset familial and sporadic Alzheimer’s disease. Neurology. 1993;43:1467-1472.; 43. Duran-Aniotz C, Orellana P, Leon Rodriguez T, et al. Systematic review: genetic, neuroimaging, and fluids biomarkers for frontotemporal dementia across Latin America countries. Front Neurol. 2021;12:663407.; 44. Sexton C, Snyder HM, Chandrasekaran L, Worley S, Carrillo MC. Expanding representation of low and middle income countries in global dementia research: commentary from the Alzheimer’s. Association Front Neurol. 2021;12:633777.; 45. Tutor CA, Frias L. Development of PET in Latin America Experience of the first PET-Cyclotron Center. World J Nucl Med. 2002;1:219.; 46. Allegri RF, Chrem Mendez P, Calandri I, et al. Prognostic value of ATN Alzheimer biomarkers: 60-month follow-up results from the Argentine Alzheimer’s Disease Neuroimaging Initiative. Alzheimers Dement (Amst). 2020;12:e12026.; 47. Allegri RF, Chrem Mendez P, Russo MJ, et al. Biomarkers of Alzheimer’s disease in mild cognitive impairment: experience in a memory clinic from Latin America. Neurologia (Engl Ed). 2021;36:201- 208.; 48. Cecchini MA, Yassuda MS, Squarzoni P, et al. Deficits in short-term memory binding are detectable in individuals with brain amyloid deposition in the absence of overt neurodegeneration in the Alzheimer’s disease continuum. Brain Cogn. 2021;152:105749.; 49. Damian A, Portugal F, Niell N, et al. Clinical impact of PET with (18)FFDG and (11)C-PIB in patients with dementia in a developing country. Front Neurol. 2021;12:630958.; 50. Faria DP, Duran FL, Squarzoni P, et al. Topography of 11C-Pittsburgh compound B uptake in Alzheimer’s disease: a voxel-based investigation of cortical and white matter regions. Braz J Psychiatry. 2019;41:101- 111.; 51. Coutinho AM, Busatto GF, de Gobbi Porto FH, et al. Brain PET amyloid and neurodegeneration biomarkers in the context of the 2018 NIA-AA research framework: an individual approach exploring clinicalbiomarker mismatches and sociodemographic parameters. Eur J Nucl Med Mol Imaging. 2020;47:2666-2680.; 52. de Souza GS, Andrade MA, Borelli WV, et al. Amyloid-beta PET classification on cognitive aging stages using the centiloid scale. Mol Imaging Biol. 2022;24:394-403.; 53. Hansen EO, Dias NS, Burgos ICB, et al. Millipore xMap(R) Luminex (HATMAG-68K): an accurate and cost-effective method for evaluating Alzheimer’s biomarkers in cerebrospinal fluid. Front Psychiatry. 2021;12:716686.; 54. Madeira C, Lourenco MV, Vargas-Lopes C, et al. d-serine levels in Alzheimer’s disease: implications for novel biomarker development. Transl Psychiatry. 2015;5:e561.; 55. Lourenco MV, Ribeiro FC, Sudo FK, et al. Cerebrospinal fluid irisin correlates with amyloid-beta, BDNF, and cognition in Alzheimer’s disease. Alzheimers Dement (Amst). 2020;12:e12034.; 56. Lourenco MV, Ribeiro FC, Santos LE, et al. Cerebrospinal fluid neurotransmitters, cytokines, and chemokines in Alzheimer’s and lewy body diseases. J Alzheimers Dis. 2021;82:1067-1074.; 57. Madeira C, Vargas-Lopes C, Brandao CO, et al. Elevated glutamate and glutamine levels in the cerebrospinal fluid of patients with probable Alzheimer’s disease and depression. Front Psychiatry. 2018;9: 561.; 58. Reiman EM, Quiroz YT, Fleisher AS, et al. Brain imaging and fluid biomarker analysis in young adults at genetic risk for autosomal dominant Alzheimer’s disease in the presenilin 1 E280A kindred: a case-control study. Lancet Neurol. 2012;11:1048-1056.; 59. Hampel H, O’Bryant SE, Molinuevo JL, et al. Blood-based biomarkers for Alzheimer disease: mapping the road to the clinic. Nat Rev Neurol. 2018;14:639-652.; 60. Ibanez A, Parra MA, Butler C, Latin A, the Caribbean Consortium on D. The Latin America and the Caribbean Consortium on Dementia (LAC-CD): from networking to research to implementation science. J Alzheimers Dis. 2021;82:S379-S394.; 61. Ibanez A, Yokoyama JS, Possin KL, et al. The multi-partner consortium to expand dementia Research in Latin America (ReDLat): driving multicentric research and implementation science. Front Neurol. 2021;12:631722.; 62. Reyes-Pablo AE, Campa-Cordoba BB, Luna-Viramontes NI, et al. National dementia BioBank: a strategy for the diagnosis and study of neurodegenerative diseases in Mexico. J Alzheimers Dis. 2020;76:853- 862.; 63. Zetterberg H, Bendlin BB. Biomarkers for Alzheimer’s diseasepreparing for a new era of disease-modifying therapies. Mol Psychiatry. 2021;26:296-308.; 64. Thijssen EH, La Joie R, Wolf A, et al. Diagnostic value of plasma phosphorylated tau181 in Alzheimer’s disease and frontotemporal lobar degeneration. Nat Med. 2020;26:387-397.; 65. Khan TK, Alkon DL. Peripheral biomarkers of Alzheimer’s disease. J Alzheimers Dis. 2015;44:729-744.; 66. Ogonowski N, Salcidua S, Leon T, et al. Systematic review: microRNAs as potential biomarkers in mild cognitive impairment diagnosis. Front Aging Neurosci. 2021;13:807764.; 67. Alawode DOT, Heslegrave AJ, Ashton NJ, et al. Transitioning from cerebrospinal fluid to blood tests to facilitate diagnosis and disease monitoring in Alzheimer’s disease. J Intern Med. 2021;290:583- 601.; 68. Wojsiat J, Laskowska-Kaszub K, Mietelska-Porowska A, Wojda U. Search for Alzheimer’s disease biomarkers in blood cells: hypothesesdriven approach. Biomark Med. 2017;11:917-931.; 69. O’Bryant SE, Mielke MM, Rissman RA, et al. Blood-based biomarkers in Alzheimer disease: current state of the science and a novel collaborative paradigm for advancing from discovery to clinic. Alzheimers Dement. 2017;13:45-58.; 70. Teunissen CE, Verberk IMW, Thijssen EH, et al. 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Shiino A, Shirakashi Y, Ishida M, Tanigaki K, Japanese Alzheimer’s Disease Neuroimaging I. Machine learning of brain structural biomarkers for Alzheimer’s disease (AD) diagnosis, prediction of disease progression, and amyloid beta deposition in the Japanese population. Alzheimers Dement (Amst). 2021;13:e12246.; 77. Zhou Y, Song Z, Han X, Li H, Tang X. Prediction of Alzheimer’s disease progression based on magnetic resonance imaging. ACS Chem Neurosci. 2021;12:4209-4223.; 78. Di Tella S, Cabinio M, Isernia S, et al. Neuroimaging biomarkers predicting the efficacy of multimodal rehabilitative intervention in the Alzheimer’s Dementia Continuum Pathology. Front Aging Neurosci. 2021;13:735508.; 79. Menon MC, Murphy B, Heeger PS. Moving biomarkers toward clinical implementation in kidney transplantation. J Am Soc Nephrol. 2017;28:735-7347.; 80. Teunissen CE, Otto M, Engelborghs S, et al. White paper by the Society for CSF Analysis and Clinical Neurochemistry: overcoming barriers in biomarker development and clinical translation. Alzheimers Res Ther. 2018;10:30.; 81. Bachli MB, Sedeno L, Ochab JK, et al. Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: a machine learning approach. Neuroimage. 2020;208:116456.; 735; 721; 19; Yonatan Sanz Perl, Sol Fittipaldi, Cecilia Gonzalez Campo, Sebastián Moguilner, Josephine Cruzat, Matias E Fraile-Vazquez, Rubén Herzog, Morten L Kringelbach, Gustavo Deco, Pavel Prado, Agustin Ibanez, Enzo Tagliazucchi, Model-based whole-brain perturbational landscape of neurodegenerative diseases, eLife, 10.7554/eLife.83970, 12, (2023).; 552-5260; https://hdl.handle.net/11323/10349Test; Corporación Universidad de la Costa; REDICUC – Repositorio CUC; https://repositorio.cuc.edu.coTest/

الإتاحة: https://doi.org/10.7554/eLife.83970Test
https://doi.org/10.1002/alz.12757Test
https://doi.org/10.7554/eLife.83970Test,
https://hdl.handle.net/11323/10349Test
https://repositorio.cuc.edu.coTest/

View record in BASE

النص الكامل

10

دورية أكاديمية

Computer-aided diagnosis for tuberculosis classification with water strider optimization algorithm

المؤلفون: Escorcia-Gutierrez, José, Soto-Diaz, Roosvel, Madera, Natasha, Soto, Carlos, Burgos-Florez, Francisco, Rodríguez, Alexander, Mansour, Romany F.

المصدر: https://www.techscience.com/csse/v46n2/51641Test.

مصطلحات موضوعية: Computer-aided diagnosis, Water strider optimization, Deep learning, Chest x-rays, Transfer learning

وصف الملف: 17 páginas; application/pdf

العلاقة: Computer Systems Science and Engineering; [1] T. Rahman, A. Khandakar, M. A. Kadir, K. R. Islam, K. F. Islam et al., “Reliable tuberculosis detection using chest x-ray with deep learning, segmentation and visualization,” IEEE Access, vol. 8, pp. 191191–191586, 2020.; [2] N. Baghdadi, A. S. Maklad, A. Malki and M. A. Deif, “Reliable sarcoidosis detection using chest x-rays with efficientnets and stain-normalization techniques,” Sensors, vol. 22, no. 10, pp. 3846, 2022.; [3] L. An, K. Peng, X. Yang, P. Huang, Y. Luo et al., “E-TBNet: Light deep neural network for automatic detection of tuberculosis with x-ray dr imaging,” Sensors, vol. 22, no. 3, pp. 821, 2022.; [4] E. Showkatian, M. Salehi, H. Ghaffari, R. Reiazi, N. Sadighi et al., “Deep learning-based automatic detection of tuberculosis disease in chest X-ray images,” Polish Journal of Radiology, vol. 87, no. 1, pp. 118, 2022.; [5] T. Khatibi, A. Shahsavari and A. Farahani, “Proposing a novel multi-instance learning model for tuberculosis recognition from chest X-ray images based on CNNs, complex networks and stacked ensemble,” Physical and Engineering Sciences in Medicine, vol. 44, no. 1, pp. 291–311, 2021.; [6] A. S. Becker, C. Blüthgen, C. S. Wiltshire, B. Castelnuovo, A. Kambugu et al., “Detection of tuberculosis patterns in digital photographs of chest X-ray images using deep learning: Feasibility study,” The International Journal of Tuberculosis and Lung Disease, vol. 22, no. 3, pp. 328–335, 2018.; [7] S. Rajaraman and S. K. Antani, “Modality-specific deep learning model ensembles toward improving tb detection in chest radiographs,” IEEE Access, vol. 8, pp. 27318–27327, 2020.; [8] R. O. Panicker, K. S. Kalmady, J. Rajan and M. K. Sabu, “Automatic detection of tuberculosis bacilli from microscopic sputum smear images using deep learning methods,” Biocybernetics and Biomedical Engineering, vol. 38, no. 3, pp. 691–699, 2018.; [9] G. Tavaziva, M. Harris, S. K. Abidi, C. Geric, M. Breuninger et al., “Chest x-ray analysis with deep learningbased software as a triage test for pulmonary tuberculosis: An individual patient data meta-analysis of diagnostic accuracy,” Clinical Infectious Diseases, vol. 74, no. 8, pp. 1390–1400, 2021.; [10] J. Escorcia-Gutierrez, K. Beleño, J. Jimenez-Cabas, M. Elhoseny, M. Dahman Alshehri et al., “An automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems,” Measurement, vol. 195, no. 10, pp. 111226, 2022.; [11] S. P. Kale, J. Patil, A. Kshirsagar and V. Bendre, “Early lungs tuberculosis detection using deep learning,” in Intelligent Sustainable Systems. Lecture Notes in Networks and Systems book series, vol. 333. Singapore: Springer, pp. 287–294, 2022.; [12] J. Escorcia-Gutierrez, J. Cuello, C. Barraza, M. Gamarra, P. Romero-Aroca et al., “Analysis of pre-trained convolutional neural network models in diabetic retinopathy detection through retinal fundus images,” in Int. Conf. on Computer Information Systems and Industrial Management, Barranquilla, Colombia, vol. 13293, pp. 202–213, 2022.; [13] J. Escorcia-Gutierrez, R. F. Mansour, K. Beleño, J. Jiménez-Cabas, M. Pérez et al., “Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images,” Computers, Materials and Continua, vol. 71, no. 3, pp. 4221–4235, 2022.; [14] K. Muthumayil, S. Manikandan, S. Srinivasan, J. Escorcia-Gutierrez, M. Gamarra et al., “Diagnosis of leukemia disease based on enhanced virtual neural network,” Computers, Materials and Continua, vol. 69, no. 2, pp. 2031–2044, 2021.; [15] S. Althubiti, J. Escorcia-Gutierrez, M. Gamarra, R. Soto-Diaz, R. F. Mansour et al., “Improved metaheuristics with machine learning enabled medical decision support system,” Computers, Materials and Continua, vol. 73, no. 2, pp. 2423–2439, 2022.; [16] S. Manikandan, S. Srinivasan, J. Escorcia-Gutiérrez, M. Gamarra and R. F. Mansour, “Diagnosis of leukemia disease based on enhanced virtual neural network,” Computers, Materials and Continua, vol. 69, no. 2, pp. 2031–2044, 2021.; [17] Q. H. Nguyen, B. P. Nguyen, S. D. Dao, B. Unnikrishnan, R. Dhingra et al., “Deep learning models for tuberculosis detection from chest x-ray images,” in 26th Int. Conf. on Telecommunications (ICT), Hanoi, Vietnam, pp. 381–385, 2019.; [18] S. J. Heo, Y. Kim, S. Yun, S. S. Lim, J. Kim et al., “Deep learning algorithms with demographic information help to detect tuberculosis in chest radiographs in annual workers’ health examination data,” International Journal of Environmental Research and Public Health, vol. 16, no. 2, pp. 250, 2019.; [19] M. H. A. Hijazi, S. K. T. Hwa, A. Bade, R. Yaakob and M. S. Jeffree, “Ensemble deep learning for tuberculosis detection using chest X-ray and canny edge detected images,” IAES International Journal of Artificial Intelligence, vol. 8, no. 4, pp. 429, 2019.; [20] J. Escorcia-Gutierrez, J. Torrents-Barrena, M. Gamarra, P. Romero-Aroca, A. Valls et al., “Convexity shape constraints for retinal blood vessel segmentation and foveal avascular zone detection,” Computers in Biology and Medicine, vol. 127, pp. 104049, 2020.; [21] S. Dey, R. Roychoudhury, S. Malakar and R. Sarkar, “An optimized fuzzy ensemble of convolutional neural networks for detecting tuberculosis from Chest X-ray images,” Applied Soft Computing, vol. 114, no. 2, pp. 108094, 2022.; [22] E. Tasci, C. Uluturk and A. Ugur, “A voting-based ensemble deep learning method focusing on image augmentation and preprocessing variations for tuberculosis detection,” Neural Computing and Applications, vol. 33, no. 22, pp. 15541–15555, 2021.; [23] S. K. T. Hwa, A. Bade, M. 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Burgos-Florez et al., "Computer-aided diagnosis for tuberculosis classification with water strider optimization algorithm," Computer Systems Science and Engineering, vol. 46, no.2, pp. 1337–1353, 2023. https://doi.org/10.32604/csse.2023.035253Test; https://hdl.handle.net/11323/10111Test; Corporación Universidad de la Costa; REDICUC - Repositorio CUC; https://repositorio.cuc.edu.coTest/

الإتاحة: https://doi.org/10.32604/csse.2023.035253Test
https://hdl.handle.net/11323/10111Test
https://repositorio.cuc.edu.coTest/

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