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ECA-Net: Efficient channel attention for deep convolutional neural networks. In: ICASSP, IEEE Int Conf Acoust Speech Signal Process - Proc, vol. 2021-June; 2021. p. 2235\u20139. doi: 10.1109\/ICASSP39728.2021.9414568.","DOI":"10.1109\/ICASSP39728.2021.9414568"},{"key":"10.1016\/j.jare.2022.08.021_b0240","doi-asserted-by":"crossref","unstructured":"Li B, Kang G, Cheng K, Zhang N. Attention-guided convolutional neural network for detecting pneumonia on chest X-rays. 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In: 36th International conference on machine learning, ICML 2019, vol. 2019-June; 2019. p. 10691\u2013700."},{"key":"10.1016\/j.jare.2022.08.021_b0300","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi A. Inception-v4, inception-ResNet and the impact of residual connections on learning. 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Sak Univ J Comput Inf Sci 2021;4(1):26\u201334. doi: 10.35377\/saucis.04.01.787030.","DOI":"10.35377\/saucis.04.01.787030"},{"issue":"4","key":"10.1016\/j.jare.2022.08.021_b0330","doi-asserted-by":"crossref","first-page":"10715","DOI":"10.1039\/C5TC02043C","article-title":"Pneumonia detection and classification using deep learning on chest X-Ray images Muazzez","volume":"3","author":"Paquin","year":"2015","journal-title":"J Mater Chem C"},{"key":"10.1016\/j.jare.2022.08.021_b0335","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2019\/4180949","article-title":"An efficient deep learning approach to pneumonia classification in healthcare","volume":"2019","author":"Stephen","year":"2019","journal-title":"J Healthc Eng"},{"issue":"3\u20134","key":"10.1016\/j.jare.2022.08.021_b0340","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1080\/00051144.2021.1973297","article-title":"Pneumonia detection in chest X-ray images using compound scaled deep learning model","volume":"62","author":"Hashmi","year":"2021","journal-title":"Automatika"},{"issue":"1","key":"10.1016\/j.jare.2022.08.021_b0345","first-page":"57","article-title":"UBNet: deep learning-based approach for automatic X-ray image detection of pneumonia and COVID-19 patients","volume":"30","author":"Widodo","year":"2022","journal-title":"J X-ray Sci Technol"},{"issue":"1","key":"10.1016\/j.jare.2022.08.021_b0350","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13634-021-00755-1","article-title":"A comparative study of multiple neural network for detection of COVID-19 on chest X-ray","volume":"2021","author":"Shazia","year":"2021","journal-title":"EURASIP J Adv Signal Process"},{"key":"10.1016\/j.jare.2022.08.021_b0355","doi-asserted-by":"crossref","unstructured":"Luj\u00e1n-Garc\u00eda JE, Y\u00e1\u00f1ez-M\u00e1rquez C, Villuendas-Rey Y, Camacho-Nieto O. 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