STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Microwave Engineering and Technologies Review Article

Pneumonia Detection and Classification Using Deep Learning

  1. Saniya P.M Department of Computer Science and Engineering, P A College of Engineering, Mangalore
  2. Mohammad Shabeer Department of Computer Science and Engineering, P A College of Engineering, Mangalore
  3. Muhammed Sinan M.S Department of Computer Science and Engineering, P A College of Engineering, Mangalore
  4. Muzammil Rahman K.M Department of Computer Science and Engineering, P A College of Engineering, Mangalore
  5. Sanad Fazal Kota Department of Computer Science and Engineering, P A College of Engineering, Mangalore

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 224x224 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

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