{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T17:06:27Z","timestamp":1787936787427,"version":"build-2784847793"},"reference-count":36,"publisher":"Termedia Sp. z.o.o.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pol J Radiol"],"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>X-ray images are viewed as a vital component in emergency diagnosis. They are often used by deep learning applications for disease prediction, especially for thoracic pathologies. Pneumonia, a fatal thoracic disease induced by bacteria or viruses, generates a pleural effusion where fluids are accumulated inside lungs, leading to breathing difficulty. The utilization of X-ray imaging for pneumonia detection offers several advantages over other modalities such as computed tomography scans or magnetic resonance imaging. X-rays provide a cost-effective and easily accessible method for screening and diagnosing pneumonia, allowing for quicker assessment and timely intervention. However, interpretation of chest X-ray images depends on the radiologist\u2019s competency. Within this study, we aim to suggest new elements leading to good interpretation of chest X-ray images for pneumonia detection, especially for distinguishing between viral and bacterial pneumonia.<\/jats:p><\/jats:sec><jats:sec><jats:title>Material and methods<\/jats:title><jats:p>We proposed an interpretation model based on convolutional neural networks (CNNs) and extreme gradient boosting (XGboost) for pneumonia classification. The experimental study is processed through various scenarios, using Python as a programming language and a public database obtained from Guangzhou Women and Children\u2019s Medical Centre.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The results demonstrate an acceptable accuracy of 87% within a mere 7 seconds, thereby endorsing its effectiveness compared to similar existing works.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our study provides a model based on CNN and XGboost to classify images of viral and bacterial pneumonia. The work is a challenging task due to the lack of appropriate data. The experimental process allows a better accuracy of 87%, a specificity of 89%, and a sensitivity of 85%.<\/jats:p><\/jats:sec>","DOI":"10.5114\/pjr.2023.132533","type":"journal-article","created":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T12:15:41Z","timestamp":1700223341000},"page":"483-493","source":"Crossref","is-referenced-by-count":15,"title":["An improvement of the CNN-XGboost model for pneumonia disease classification"],"prefix":"10.5114","volume":"88","author":[{"given":"Yousra","family":"Hedhoud","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tahar","family":"Mekhaznia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Amroune","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"3061","published-online":{"date-parts":[[2023,10,25]]},"reference":[{"key":"ref-1157702","doi-asserted-by":"publisher","DOI":"10.1002\/14651858.CD006088.PUB4\/FULL"},{"key":"ref-1157703","article-title":"World Pneumonia Day \u2013 Stop Pneumonia\/Every Breath Counts","unstructured":"World Pneumonia Day \u2013 Stop Pneumonia\/Every Breath Counts. 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