Multi-layer stacked residual coordinate termite alate network for multi-class lung diseases detection from chest X-ray images

The World Health Organization identifies COVID-19, pneumonia, tuberculosis, and pneumothorax among the higher effects of death worldwide. Common symptoms include shortness of breath, fever, sneezing, and coughing. Traditional diagnostic methods such as thorough blood counts, the Mantoux skin test, antibodies, and DNA testing are often time-consuming and have a sensitivity of only about 80 %, with a 20 % error rate. As a faster and more reliable alternative, chest X-ray imaging is increasingly used for the detection of lung diseases. This research proposed a new system called the Multi-Layer Stacked Residual Coordinate Network, designed to accurately classify various lung diseases using chest X-ray images. Images are drawn from 6 datasets that are openly accessible. To enhance image quality, we develop the Gaussian Fourier Pyramid for Local Laplacian Filter technique, which combines adaptive histogram equalization with a multi-resolution Gaussian pyramid to better highlight important lung features while reducing noise. Next, the Mantis Search algorithm, a novel thresholding method, is used to segment critical regions in the X-rays, focusing the analysis on the most relevant areas. For deeper feature extraction, the model employed a Multi-Generative Adversarial Transformer, which captures complex patterns in the segmented regions. Finally, for classification, the Multi-Layer Stacked Residual Coordinate Network is optimized using Termite Alate Optimization, a metaheuristic inspired by termite foraging behavior that fine-tunes network parameters for better accuracy. According to experimental findings, the suggested method achieves 99 % accuracy, significantly outperforming existing techniques for multiclass lung disease classification.