Pneumonia Detection and Classification Using Deep Learning

Year : 2024 | Volume : 11 | Issue : 03 | Page : 9 19
    By

    Saniya P.M,

  • Mohammad Shabeer,

  • Muhammed Sinan M.S,

  • Muzammil Rahman K.M,

  • Sanad Fazal Kota,

  1. Assistant Professor, Department of Computer Science and Engineering, P A College of Engineering, Mangalore, Karnataka, India
  2. Student, Department of Computer Science and Engineering, P A College of Engineering, Mangalore, Karnataka, India
  3. Student, Department of Computer Science and Engineering, P A College of Engineering, Mangalore, Karnataka, India
  4. Student, Department of Computer Science and Engineering, P A College of Engineering, Mangalore, Karnataka, India
  5. Student, Department of Computer Science and Engineering, P A College of Engineering, Mangalore, Karnataka, India

Abstract

Pneumonia, an infectious lung disease primarily caused by bacteria, often exacerbated by environmental factors, leads to the accumulation of pus in the lung’s alveoli. Accurate diagnosis through chest X-rays, ultrasounds, or lung biopsies is crucial to avoid misdiagnosis and ensure proper treatment, crucial for patients’ quality of life. Diagnostic capacities have been greatly improved by deep learning advances, especially with convolutional neural networks (CNNs). This research presents a robust CNN-based approach for predicting and detecting pneumonia from chest X-ray images. Using a dataset comprising 20,000 images at a resolution of 224×224 and trained with a batch size of 32, the CNN model achieved an impressive 95% accuracy during training. The study demonstrates the CNN model’s effectiveness in identifying COVID-19, bacterial, and viral pneumonia solely from chest X-ray images, highlighting its potential for accurate diagnosis in clinical settings. It was widely believed at the time that computers would eventually become as adaptive as humans. The fundamental idea behind adaptive learning is that the system or tool may adapt to the user’s or student’s preferred learning style, giving them a better and more efficient learning experience.

Keywords: Pneumonia detection, adaptive deep learning, deep convolutional neural network architecture

[This article belongs to Journal of Microwave Engineering and Technologies ]

How to cite this article:
Saniya P.M, Mohammad Shabeer, Muhammed Sinan M.S, Muzammil Rahman K.M, Sanad Fazal Kota. Pneumonia Detection and Classification Using Deep Learning. Journal of Microwave Engineering and Technologies. 2024; 11(03):9-19.
How to cite this URL:
Saniya P.M, Mohammad Shabeer, Muhammed Sinan M.S, Muzammil Rahman K.M, Sanad Fazal Kota. Pneumonia Detection and Classification Using Deep Learning. Journal of Microwave Engineering and Technologies. 2024; 11(03):9-19. Available from: https://journals.stmjournals.com/jomet/article=2024/view=177491


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Regular Issue Subscription Review Article
Volume 11
Issue 03
Received 10/08/2024
Accepted 26/08/2024
Published 06/09/2024
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