Classification of chest X-Ray images using novel adaptive Morphological neural networks

Shaobo Liu, Frank Y. Shih, Xin Zhong

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The chest X-ray images are difficult to classify for the radiologists due to the noisy nature. The existing models based on convolutional neural networks contain a giant number of parameters, and thus require multi-advanced GPUs to deploy. In this paper, we are the first to develop the adaptive morphological neural networks to classify chest X-ray images, such as pneumonia and COVID-19. A novel structure, which can self-learn morphological dilation and erosion, is proposed to determine the most suitable depth of the adaptive layer. Experimental results on the chest X-ray and the COVID-19 datasets show that the proposed model can achieve the highest classification rate as compared against the existing models. Moreover, it can significantly reduce the computational parameters of the existing models by 97%. The advantage makes the developed model more attractive than others to deploy in the internet and other device platforms.

Original languageEnglish (US)
Article number2157006
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume35
Issue number10
DOIs
StatePublished - Aug 1 2021

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Keywords

  • Chest X-ray
  • COVID-19
  • Deep learning
  • Mathematical morphology
  • Morphological neural network
  • Pneumonia classification

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