Detection and Interpretation of X-Ray Scans for the Presence of Pneumonia Using Convolutional Neural Network

Authors

  • Peter Oluwasayo Adigun Department of Computer Science, New Mexico Highlands University, 1005 Diamond St, Las Vegas, New Mexico, USA
  • Ayodeji Adedotun Adeniyi Department of Media, Art, and Technology, New Mexico Highlands University, 1005 Diamond St, Las Vegas, New Mexico, USA
  • Tobi Titus Oyekanmi Department of Media, Art, and Technology, New Mexico Highlands University, 1005 Diamond St, Las Vegas, New Mexico, USA

Keywords:

Machine learning (ML), Convolutional Neural Network (CNN), Pneumonia Disease (PD)

Abstract

Convolutional neural network’s application is essentially an impactful technology to proffering solutions in medical diagnostics. This research carried out a design and implementation of a medical imaging analysis and classification of X-ray scans of pneumonia images using a convolutional neural network. The CNN system was designed using an algorithm of a convolutional neural network. The designed CNN system was processed by uploading 5,216 data which comprised normal and pneumonic image scans. The CNN system was trained with 5,000 datasets and tested. The findings from the study established that the implemented system based on a convolutional neural network algorithm is 76% accurate. This study is subjected to further studies.

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Published

2025-02-03

How to Cite

Peter Oluwasayo Adigun, Ayodeji Adedotun Adeniyi, & Tobi Titus Oyekanmi. (2025). Detection and Interpretation of X-Ray Scans for the Presence of Pneumonia Using Convolutional Neural Network. American Scientific Research Journal for Engineering, Technology, and Sciences, 101(1), 97–108. Retrieved from https://asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/11430

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