Cascaded Deep Learning Model for Detecting Lung Infections Using Chest X-Rays

Authors

  • Akash Chaturvedi
  • Prof. Shivank Soni

DOI:

https://doi.org/10.24113/ijoscience.v10i4.498

Abstract

This work focuses on efforts for accurately predicting lung diseases like omicron and pneumonia using chest X-ray imaging, a reliable method in this domain. The work adopts a transfer learning model for lung infection predictions from chest X-ray images. The proposed architecture encompasses both training and testing functions, with key steps including pre-processing, deep feature extraction, and classification. Initially, each X-ray image is enhanced through digital filtering for quality improvement. These processed images are then input into a robust, step-wise learning model that efficiently facilitates the automatic learning of features. The highlight of this approach is the Cascaded learning model, which not only achieves a high accuracy rate of 99% but also significantly reduces computational complexity. This is evidenced by a lower number of training parameters, making the model both more efficient and lightweight, and hence more practical for clinical applications in differentiating between omicron and pneumonia.

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Author Biographies

Akash Chaturvedi

M.Tech Scholar

Department of CSE

Oriental Group of Institutes,

Bhopal, M.P., India

Prof. Shivank Soni

Assistant Professor

Department of CSE

Oriental Group of Institutes,

Bhopal, M.P., India

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Published

04/10/2024

How to Cite

Chaturvedi, A., & Soni, P. S. (2024). Cascaded Deep Learning Model for Detecting Lung Infections Using Chest X-Rays. SMART MOVES JOURNAL IJOSCIENCE, 10(4), 1–7. https://doi.org/10.24113/ijoscience.v10i4.498

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Articles