Machine learning and machine learned prediction in chest X-ray images

Authors

  • Shereiff M. Garrett Department of Chemistry, Physics and Materials Science, Fayetteville State University, Fayetteville, NC 28301, USA
  • Abhinav Adhikari Department of Mathematical and Statistical Sciences, University of Nebraska Omaha, Omaha, NE 68182, USA
  • Sarina Gautam Jack Britt High School, Fayetteville, NC 28306, USA
  • Da’Shawn M. Morris Department of Chemistry, Physics and Materials Science, Fayetteville State University, Fayetteville, NC 28301, USA
  • Laxmi Shah Pediatric Intensive Care Unit, Kanti Children’s Hospital, Kathmandu 44600, Nepal
  • Chandra M. Adhikari Department of Chemistry, Physics and Materials Science, Fayetteville State University, Fayetteville, NC 28301, USA

DOI:

https://doi.org/10.3126/jnphyssoc.v11i1.87420

Keywords:

Machine learning, Artificial intelligence, Convolutional neural network, DenseNet121, Pneumonia detection, Grad-CAM

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.

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Published

2025-12-18

How to Cite

Garrett, S. M., Adhikari, A., Gautam, S., Morris, D. M., Shah, L., & Adhikari, C. M. (2025). Machine learning and machine learned prediction in chest X-ray images. Journal of Nepal Physical Society, 11(1), 22–30. https://doi.org/10.3126/jnphyssoc.v11i1.87420

Issue

Vol. 11 No. 1 (2025): Special Issue: ANPA Conference 2025

Section

Articles

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