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Chest X-ray Radiography (CXR) holds significant importance in medical imaging for identifying critical illnesses. While conventional diagnosis relied on visual examination by radiologists, inherent patterns in thoracic diseases have made this method prone to human error, leading to inaccurate diagnoses. Recent developments in the fields of Machine Learning (ML) and Deep Learning (DL) have revolutionized analysis of CXR images, offering efficient and rapid detection methods. Various contemporary DL models, including VGG, ResNet, DenseNet, InceptionNet, EfficientNet, and ensemble learning techniques, have shown promising results in chest disease detection. This paper presents an overview of the latest DL solutions for pneumonia and COVID-19 detection, highlighting recent trends, available datasets, guidelines for implementing DL processes, existing challenges, and prospective research directions. The synthesized findings aim to aid researchers and developers in navigating their work within this domain effectively.

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Exploring Recent Developments in Radiographic Chest Disease Detection Through Deep Learning Models

  • 章节

  • 发表日期：

    2025年

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  • A. Beena Godbin,
  • S. Graceline Jasmine,
  • S. K. Narendranathan

摘要

Chest X-ray Radiography (CXR) holds significant importance in medical imaging for identifying critical illnesses. While conventional diagnosis relied on visual examination by radiologists, inherent patterns in thoracic diseases have made this method prone to human error, leading to inaccurate diagnoses. Recent developments in the fields of Machine Learning (ML) and Deep Learning (DL) have revolutionized analysis of CXR images, offering efficient and rapid detection methods. Various contemporary DL models, including VGG, ResNet, DenseNet, InceptionNet, EfficientNet, and ensemble learning techniques, have shown promising results in chest disease detection. This paper presents an overview of the latest DL solutions for pneumonia and COVID-19 detection, highlighting recent trends, available datasets, guidelines for implementing DL processes, existing challenges, and prospective research directions. The synthesized findings aim to aid researchers and developers in navigating their work within this domain effectively.