{"ID":2888337,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.23455","arxiv_id":"2507.23455","title":"Machine learning and machine learned prediction in chest X-ray images","abstract":"Machine learning and artificial intelligence are fast-growing fields of research in which data is used to train algorithms, learn patterns, and make predictions. This approach helps to solve seemingly intricate problems with significant accuracy without explicit programming by recognizing complex relationships in data. Taking an example of 5824 chest X-ray images, we implement two machine learning algorithms, namely, a baseline convolutional neural network (CNN) and a DenseNet-121, and present our analysis in making machine-learned predictions in predicting patients with ailments. Both baseline CNN and DenseNet-121 perform very well in the binary classification problem presented in this work. Gradient-weighted class activation mapping shows that DenseNet-121 correctly focuses on essential parts of the input chest X-ray images in its decision-making more than the baseline CNN.","short_abstract":"Machine learning and artificial intelligence are fast-growing fields of research in which data is used to train algorithms, learn patterns, and make predictions. This approach helps to solve seemingly intricate problems with significant accuracy without explicit programming by recognizing complex relationships in data....","url_abs":"https://arxiv.org/abs/2507.23455","url_pdf":"https://arxiv.org/pdf/2507.23455v1","authors":"[\"Shereiff Garrett\",\"Abhinav Adhikari\",\"Sarina Gautam\",\"DaShawn Marquis Morris\",\"Chandra Mani Adhikari\"]","published":"2025-07-31T11:31:25Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}