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Caobelli, \u201cArtificial intelligence in medical imaging: Game over\nfor radiologists?\u201d European journal of radiology, vol. 126, 2020. https:\/\/dx.doi.org\/10.1016\/j.ejrad.2020.108940","DOI":"10.1016\/j.ejrad.2020.108940"},{"key":"ref3","doi-asserted-by":"publisher","unstructured":"G. Liebel, P. V. Dias, I. J. C. Schneider, A. R. d. S\u00e1 Junior, A. Hentz,\nC. d. S. Ferreira, and A. Chaoubah, \u201cAnalysis of expenses with diagnostic imaging in brazil,\u201d Cadernos Sa\u00fade Coletiva, vol. 29, pp. 453\u2013463,\n2021. https:\/\/dx.doi.org\/10.1590\/1414-462X202129030397","DOI":"10.1590\/1414-462X202129030397"},{"key":"ref4","doi-asserted-by":"publisher","unstructured":"S. Sharma and K. Guleria, \u201cA systematic literature review on deep\nlearning approaches for pneumonia detection using chest x-ray images,\u201d\nMultimedia Tools and Applications, vol. 83, no. 8, pp. 24 101\u201324 151,\n2024. https:\/\/dx.doi.org\/10.1007\/s11042-023-16419-1","DOI":"10.1007\/s11042-023-16419-1"},{"key":"ref5","doi-asserted-by":"publisher","unstructured":"W. Liawrungrueang, I. Han, W. Cholamjiak, P. Sarasombath, and K. D.\nRiew, \u201cArtificial intelligence detection of cervical spine fractures using\nconvolutional neural network models,\u201d Neurospine, vol. 21, no. 3, p.\n833, 2024. https:\/\/dx.doi.org\/10.14245\/ns.2448580.290","DOI":"10.14245\/ns.2448580.290"},{"key":"ref6","doi-asserted-by":"publisher","unstructured":"M. To\u011fa\u00e7ar, B. Ergen, Z. C\u00f6mert, and F. \u00d6zyurt, \u201cA deep feature learning\nmodel for pneumonia detection applying a combination of mrmr feature\nselection and machine learning models,\u201d Irbm, vol. 41, no. 4, pp. 212\u2013222, 2020. https:\/\/dx.doi.org\/10.1016\/j.irbm.2019.10.006","DOI":"10.1016\/j.irbm.2019.10.006"},{"key":"ref7","doi-asserted-by":"publisher","unstructured":"A. Mabrouk, R. P. Diaz Redondo, A. Dahou, M. Abd Elaziz, and\nM. Kayed, \u201cPneumonia detection on chest x-ray images using ensemble\nof deep convolutional neural networks,\u201d Applied Sciences, vol. 12,\nno. 13, p. 6448, 2022. https:\/\/dx.doi.org\/10.3390\/app12136448","DOI":"10.3390\/app12136448"},{"key":"ref8","doi-asserted-by":"publisher","unstructured":"A. Akgundogdu, \u201cDetection of pneumonia in chest x-ray images by\nusing 2d discrete wavelet feature extraction with random forest,\u201d Inter-national Journal of Imaging Systems and Technology, vol. 31, no. 1, pp.\n82\u201393, 2021. https:\/\/dx.doi.org\/10.1002\/ima.22501","DOI":"10.1002\/ima.22501"},{"key":"ref9","doi-asserted-by":"publisher","unstructured":"D. Varshni, K. Thakral, L. Agarwal, R. Nijhawan, and A. Mittal, \u201cPneumonia detection using cnn based feature extraction,\u201d in 2019 IEEE international conference on electrical, computer and communication technologies (ICECCT). IEEE, 2019. https:\/\/dx.doi.org\/10.1109\/ICECCT.2019.8869364\npp. 1\u20137.","DOI":"10.1109\/ICECCT.2019.8869364"},{"key":"ref10","doi-asserted-by":"publisher","unstructured":"F. G. da Silva, L. P. Ramos, B. G. Palm, and R. Machado, \u201cAssessment\nof machine learning techniques for oil rig classification in c-band\nsar images,\u201d Remote Sensing, vol. 14, no. 13, p. 2966, 2022. https:\/\/dx.doi.org\/10.3390\/rs14132966\n5 https:\/\/github.com\/GabrieleAraujo\/pneumonia_detection_cnn-ml.git","DOI":"10.3390\/rs14132966"},{"key":"ref11","doi-asserted-by":"publisher","unstructured":"D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L.\nBaxter, A. McKeown, G. Yang, X. Wu, F. Yan et al., \u201cIdentifying medical diagnoses and treatable diseases by image-based deep learning,\u201d cell,\nvol. 172, no. 5, pp. 1122\u20131131, 2018. https:\/\/dx.doi.org\/10.1016\/j.cell.2018.02.010","DOI":"10.1016\/j.cell.2018.02.010"},{"key":"ref12","doi-asserted-by":"publisher","unstructured":"J. O. Diniz, D. A. Dias Jr, L. B. da Cruz, D. L. Gomes Jr, O. A. Cort\u00eas,\nand A. O. de Carvalho Filho, \u201cEfficientensemble: Diagn\u00f3stico de c\u00e2ncer\nde mama em imagens de ultrassom utilizando processamento de imagens\ne ensemble de efficientnets,\u201d in Simp\u00f3sio Brasileiro de Computa\u00e7\u00e3o\nAplicada \u00e0 Sa\u00fade (SBCAS). SBC, 2024. https:\/\/dx.doi.org\/10.5753\/sbcas.2024.2155","DOI":"10.5753\/sbcas.2024.2155"},{"key":"ref13","doi-asserted-by":"publisher","unstructured":"O. M. El Zein, M. M. Soliman, A. Elkholy, and N. I. Ghali, \u201cTransfer\nlearning based model for pneumonia detection in chest x-ray images,\u201d\nInternational Journal of Intelligent Engineering and Systems, vol. 14,\nno. 5, pp. 56\u201366, 2021. https:\/\/dx.doi.org\/10.22266\/ijies2021.1031.06","DOI":"10.22266\/ijies2021.1031.06"},{"key":"ref14","doi-asserted-by":"publisher","unstructured":"C. Munzlinger, I. Yepes, and R. Rieder, \u201cUso de uma rede neural\nconvolucional para an\u00e1lise de exames de radiografia de pulmao com\ndetec\u00e7ao de covid-19, pneumonia e tuberculose,\u201d in Anais Estendidos\ndo XXIII Simp\u00f3sio Brasileiro de Computa\u00e7\u00e3o Aplicada \u00e0 Sa\u00fade. SBC,\n2023. https:\/\/dx.doi.org\/10.5753\/sbcas_estendido.2023.229629 pp. 25\u201330.","DOI":"10.5753\/sbcas_estendido.2023.229629"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Y. Wu, N. Japkowicz, S. Gilbert, and R. Corizzo, \u201cAttention-based\nmedical knowledge injection in deep image classification models,\u201d in\n2024 International Joint Conference on Neural Networks (IJCNN).\nIEEE, 2024, pp. 1\u20138.","DOI":"10.1109\/IJCNN60899.2024.10651246"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"J. Rocha, S. C. Pereira, J. Pedrosa, A. Campilho, and A. M. Mendon\u00e7a,\n\u201cStern: Attention-driven spatial transformer network for abnormality\ndetection in chest x-ray images,\u201d Artificial Intelligence in Medicine, vol.\n147, p. 102737, 2024.","DOI":"10.1016\/j.artmed.2023.102737"},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"C. J. Ejiyi, Z. Qin, A. O. Nnani, F. Deng, T. U. Ejiyi, M. B. Ejiyi, V. K.\nAgbesi, and O. Bamisile, \u201cResfeanet: Resnet-fused external attention\nnetwork for tuberculosis diagnosis using chest x-ray images,\u201d Computer\nMethods and Programs in Biomedicine Update, vol. 5, p. 100133, 2024.","DOI":"10.1016\/j.cmpbup.2023.100133"},{"key":"ref18","doi-asserted-by":"publisher","unstructured":"M. Bartosiewicz, M. Iwanowski, M. Wiszniewska, K. Fr\u0105czak,\nP. Le\u015bnowolski, \u201cOn combining image features and word embeddings for\nimage captioning,\u201d in 2023 18th Conference on Computer Science and\nIntelligence Systems (FedCSIS). IEEE, 2023. https:\/\/dx.doi.org\/10.15439\/2023F997\npp. 355\u2013365.","DOI":"10.15439\/2023F997"},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"C. Shorten and T. M. Khoshgoftaar, \u201cA survey on image data augmentation for deep learning,\u201d Journal of big data, vol. 6, no. 1, pp. 1\u201348,\n2019.","DOI":"10.1186\/s40537-019-0197-0"},{"key":"ref20","doi-asserted-by":"publisher","unstructured":"D. Kermany, \u201cLabeled optical coherence tomography (oct) and chest x-ray images for classification,\u201d Mendeley data, 2018. https:\/\/dx.doi.org\/10.17632\/rscb-jbr9sj.2","DOI":"10.17632\/rscb-jbr9sj.2"},{"key":"ref21","doi-asserted-by":"publisher","unstructured":"O. O. Abayomi-Alli, R. Dama\u0161evi\u010dius, R. Maskeli\u016bnas, and\nA. 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Kl\u00e9n, \u201cEvaluation metrics and statistical\ntests for machine learning,\u201d Scientific Reports, vol. 14, no. 1, p. 6086,\n2024.","DOI":"10.1038\/s41598-024-56706-x"},{"key":"ref25","doi-asserted-by":"publisher","unstructured":"W. H. Kruskal and W. A. Wallis, \u201cUse of ranks in one-criterion variance\nanalysis,\u201d Journal of the American statistical Association, vol. 47, no.\n260, pp. 583\u2013621, 1952. https:\/\/dx.doi.org\/10.1080\/01621459.1952.10483441","DOI":"10.1080\/01621459.1952.10483441"},{"key":"ref26","doi-asserted-by":"crossref","unstructured":"P. E. McKight and J. Najab, \u201cKruskal-wallis test,\u201d The\ncorsini encyclopedia of psychology, pp. 1\u20131, 2010. doi:\n10.1002\/9780470479216.corpsy0491","DOI":"10.1002\/9780470479216.corpsy0491"},{"key":"ref27","doi-asserted-by":"publisher","unstructured":"E. Ostertagova, O. Ostertag, and J. Kov\u00e1\u010d, \u201cMethodology and application\nof the kruskal-wallis test,\u201d Applied mechanics and materials, vol. 611,\npp. 115\u2013120, 2014. https:\/\/dx.doi.org\/10.4028\/www.scientific.net\/AMM.611.115","DOI":"10.4028\/www.scientific.net\/AMM.611.115"},{"key":"ref28","doi-asserted-by":"crossref","unstructured":"O. J. Dunn, \u201cMultiple comparisons using rank sums,\u201d Technometrics,\nvol. 6, no. 3, pp. 241\u2013252, 1964.","DOI":"10.1080\/00401706.1964.10490181"},{"key":"ref29","doi-asserted-by":"publisher","unstructured":"M. A. Jamil and S. Khanam, \u201cInfluence of one-way anova and Kruskal-Wallis based feature ranking on the performance of ml classifiers for\nbearing fault diagnosis,\u201d Journal of Vibration Engineering & Technologies, vol. 12, no. 3, pp. 3101\u20133132, 2024. https:\/\/dx.doi.org\/10.1007\/s42417-023-01036-x","DOI":"10.1007\/s42417-023-01036-x"},{"key":"ref30","doi-asserted-by":"crossref","unstructured":"Y. Yang, H. Lv, and N. Chen, \u201cA survey on ensemble learning under\nthe era of deep learning,\u201d Artificial Intelligence Review, vol. 56, no. 6,\npp. 5545\u20135589, 2023.","DOI":"10.1007\/s10462-022-10283-5"},{"key":"ref31","doi-asserted-by":"publisher","unstructured":"M. R. Munaf\u00f2, B. A. Nosek, D. V. Bishop, K. S. Button, C. D.\nChambers, N. Percie du Sert, U. Simonsohn, E.-J. Wagenmakers, J. J.\nWare, and J. P. Ioannidis, \u201cA manifesto for reproducible science,\u201d Nature\nhuman behaviour, vol. 1, no. 1, p. 0021, 2017. https:\/\/dx.doi.org\/10.1038\/s41562-016-0021","DOI":"10.1038\/s41562-016-0021"},{"key":"ref32","doi-asserted-by":"publisher","unstructured":"D. Donoho, \u201c50 years of data science,\u201d Journal of Computational\nand Graphical Statistics, vol. 26, no. 4, pp. 745\u2013766, 2017. https:\/\/dx.doi.org\/10.1080\/10618600.2017.1384734","DOI":"10.1080\/10618600.2017.1384734"}],"event":{"name":"20th Conference on Computer Science and Intelligence Systems (FedCSIS)","theme":"Computer Science and Intelligence Systems","location":"Krak\u00f3w, Poland","acronym":"FedCSIS","number":"20","start":{"date-parts":[[2025,9,14]]},"end":{"date-parts":[[2025,9,17]]}},"container-title":["Annals of Computer Science and Information Systems","Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS)"],"original-title":[],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T07:48:29Z","timestamp":1761119309000},"score":1,"resource":{"primary":{"URL":"https:\/\/annals-csis.org\/Volume_43\/drp\/4310.html"}},"subtitle":[],"proceedings-subject":"Computer Science and Information Systems","short-title":[],"issued":{"date-parts":[[2025,10,15]]},"references-count":32,"URL":"https:\/\/doi.org\/10.15439\/2025f4310","relation":{},"ISSN":["2300-5963"],"issn-type":[{"value":"2300-5963","type":"print"}],"subject":[],"published":{"date-parts":[[2025,10,15]]}}}