Improved swin transformer-based thorax disease classification with optimal feature selection using chest X-ray.
Authors
Affiliations (6)
Affiliations (6)
- Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia.
- Research and Innovation Centre, Agency of Information and Communication Technology, Bamako, Mali.
- Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, Saudi Arabia.
- Department of Electrical and Electronics Engineering, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia.
- Department of Computer and Information, Applied College, Jazan University, Jazan, Saudi Arabia.
- Department of Computer Science and Information, Applied College, Taibah University, Madinah, Saudi Arabia.
Abstract
Thoracic diseases, including pneumonia, tuberculosis, lung cancer, and others, pose significant health risks and require timely and accurate diagnosis to ensure proper treatment. Thus, in this research, a model for thorax disease classification using Chest X-rays is proposed by considering deep learning model. The input is pre-processed by resizing, normalizing pixel values, and applying data augmentation to address the issue of imbalanced datasets and improve model generalization. Significant features are extracted from the images using an Enhanced Auto-Encoder (EnAE) model, which combines a stacked auto-encoder architecture with an attention module to enhance feature representation and classification accuracy. To further improve feature selection, we utilize the Chaotic Whale Optimization (ChWO) Algorithm, which optimally selects the most relevant attributes from the extracted features. Finally, the disease classification is performed using the novel Improved Swin Transformer (IMSTrans) model, which is designed to efficiently process high-dimensional medical image data and achieve superior classification performance. The proposed EnAE + ChWO+IMSTrans model for thorax disease classification was evaluated using extensive Chest X-ray datasets and the Lung Disease Dataset. The proposed method demonstrates enhanced Accuracy, Precision, Recall, F-Score, MCC and MAE of 0.964, 0.977, 0.9845, 0.964, 0.9647, and 0.184 respectively indicating the reliable and efficient solution for thorax disease classification.