A Multi-Chest Disease Detection Using Multi-Scale Alignment Graph Capsule Binary Light Spectrum Dual Attention Neural Network

Authors

  • A. Pavani Author
  • A. Rajashekar Author
  • B.Ravindar Reddy Author

DOI:

https://doi.org/10.64252/kdd0bh58

Keywords:

Chest x-rays, Coronavirus, Multi-class diseases, Multi-scale Alignment, Semantic segmentation

Abstract

Internationally, the COVID-19 disease has badly affected both the healthcare system and the market. The complexity arises from the similarity in symptoms between COVID-19 and other chest diseases like pneumonia and lung cancer, making accurate diagnosis challenging. Dedicated frontline medical professionals and researchers are actively striving to develop a rapid and automated method for the initial stage recognition of COVID-19, with the goal of saving lives. Nevertheless, the clinical diagnosis of coronavirus remains subjective and variable. To tackle these issues, in this research propose a novel Multi-scale Alignment graph Capsule Binary light spectrum Dual attention neural Network (MACBDN) technique for efficiently classifying multi-chest diseases, including pneumothorax, pneumonia, tuberculosis, lung cancer and COVID-19. The methodology begins with the utilization of the adaptive guided multi-layer side window box filter for preprocessing, aiming to eliminate noise while preserving crucial details. Subsequently, a lightweight multi SegNet is employed for semantic segmentation. S-transform and fast discrete orthonormal transform are then applied for efficient feature extraction. The proposed MACBDN effectively classifies diseases using the binary light spectrum optimizer.

Downloads

Download data is not yet available.

Downloads

Published

2025-07-02

Issue

Section

Articles

How to Cite

A Multi-Chest Disease Detection Using Multi-Scale Alignment Graph Capsule Binary Light Spectrum Dual Attention Neural Network. (2025). International Journal of Environmental Sciences, 1854-1862. https://doi.org/10.64252/kdd0bh58