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Pneumonia is a prevalent illness caused by several microbiological organisms, including viruses, fungi, and bacteria. Early diagnosis is essential for a successful therapeutic approach. Computer-aided diagnosis systems assist clinicians in identifying the ailment more precisely. In this study, we use nine well-known convolutional neural network (CNN) models for the diagnosis of pneumonia, namely the CNN model 1, CNN model 2, MobileNetv2 (version 2) Model, EfficientNetB7 Model, InceptionV3 (version 3) Model, Residual Neural Network (ResNet)—101 Model, ResNet50 Model, Visual Geometry Group-19(VGG19) Model, and Xception Model. We analyze the models utilizing F1 score and accuracy. As a result, we discover that CNN Model 1 outperforms the other models, with F1 score of 91.85% and accuracy of 91.89%.

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Comparative Review for Pneumonia Detection Using Deep Learning Models

  • 会议论文

  • 发表日期：

    2025年

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  • Trishla Vagrecha,
  • Anushka Garg,
  • Shruti Bansal,
  • Ritika Kumari,
  • Poonam Bansal,
  • Amita Dev

摘要

Pneumonia is a prevalent illness caused by several microbiological organisms, including viruses, fungi, and bacteria. Early diagnosis is essential for a successful therapeutic approach. Computer-aided diagnosis systems assist clinicians in identifying the ailment more precisely. In this study, we use nine well-known convolutional neural network (CNN) models for the diagnosis of pneumonia, namely the CNN model 1, CNN model 2, MobileNetv2 (version 2) Model, EfficientNetB7 Model, InceptionV3 (version 3) Model, Residual Neural Network (ResNet)—101 Model, ResNet50 Model, Visual Geometry Group-19(VGG19) Model, and Xception Model. We analyze the models utilizing F1 score and accuracy. As a result, we discover that CNN Model 1 outperforms the other models, with F1 score of 91.85% and accuracy of 91.89%.