A deep learning-based x-ray imaging diagnosis system for classification of tuberculosis, COVID-19, and pneumonia traits using evolutionary algorithm

Abstract

To aid in detection of tuberculosis, researchers have concentrated on developing computer-aided diagnostic technologies based on x-ray imaging. Since it generates noninvasive standard-of-care data, a chest x-ray image is one of the most often used diagnostic imaging modalities in computer-aided solutions. Due to their significant interclass similarities and low intra-class variation abnormalities, chest x-ray pictures continue to pose difficulty for proper diagnosis. In this paper, a novel automated framework is proposed for the classification of tuberculosis, COVID-19, and pneumonia from chest x-ray images using deep learning and improved optimization technique. Two pre-trained convolutional neural network models such as EfficientB0 and ResNet50 have been utilized and fine-tuned based on the additional layers. Both models are trained with fixed hyperparameters on the selected datasets and obtained newly trained models. A novel feature selection technique has been proposed that selects the best features. In the novel version, distance and update position formulation has been modified. The selected features are further fused using a novel technique that is based on the serial and standard deviation threshold function. The experimental process of the proposed framework is conducted on three datasets and obtained an accuracy of 98.2%, 99.0%, and 98.7%, respectively. In addition, a detailed Wilcoxon signed-rank analysis is conducted and shows the proposed method significance performance. Based on the results, it is concluded that the proposed method accuracy is improved after the fusion process. In addition, the comparison with recent techniques shows the proposed method as more significant in terms of accuracy and precision rate.