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Improving the Quality of Chest X-Ray images for better Classification using Convolutional Neural Network

  • Unique Paper ID: 173310
  • Volume: 11
  • Issue: 9
  • PageNo: 2727-2730
  • Abstract:
  • Medical imaging plays a crucial role in diagnosing various diseases, including respiratory disorders. However, noise artifacts such as salt-and-pepper noise and Gaussian noise significantly degrade the quality of chest X-ray (CXR) images, potentially leading to inaccurate diagnoses. This research focuses on enhancing CXR images by applying noise removal techniques followed by histogram equalization to improve image quality. Two datasets are utilized: one from a public domain and another collected from laboratories. The latter undergoes a manual noise removal process to ensure enhanced image clarity. Subsequently, a Convolutional Neural Network (CNN) model, specifically ResNet-50, is applied to both datasets for classification. Comparative analysis is performed to demonstrate that manually denoised images yield better accuracy than raw noisy images. The experimental results validate the effectiveness of the proposed approach in improving image quality and diagnostic accuracy.
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  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 9
  • PageNo: 2727-2730

Improving the Quality of Chest X-Ray images for better Classification using Convolutional Neural Network

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Journal no 47859

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