Abstract
Lung disease is an infection that causes chronic inflammation of the human lung cells, which is one of the major causes of death around the world. Thoracic X-ray medical image is a well-known cheap screening approach used for lung disease detection. Deep learning networks, which are used to identify disease features in X-rays medical images, diagnosing a variety of lung diseases, are playing an increasingly important role in assisting clinical diagnosis. This paper proposes an explainable transformer with a hybrid network structure (LungMaxViT) combining CNN initial stage block with SE block to improve feature recognition for predicting Chest X-ray images for multiple lung disease classification. We contrast four classical pre-training models (ResNet50, MobileNetV2, ViT and MaxViT) through transfer learning based on two public datasets. The LungMaxVit, based on maxvit pre-trained with ImageNet 1K datasets, is a hybrid transformer with fine-tuning hyperparameters on the both X-ray datasets. The LungMaxVit outperforms all the four mentioned models, achieving a classification accuracy of 96.8%, AUC scores of 98.3%, and F1 scores of 96.7% on the COVID-19 dataset, while AUC scores of 93.2% and F1 scores of 70.7% on the Chest X-ray 14 dataset. The LungMaxVit distinguishes by its superior performance in terms of Accuracy, AUC and F1-score compared with other hybrids Networks. Several enhancement techniques, such as CLAHE, flipping and denoising, are employed to improve the classification performance of our study. The Grad-CAM visual technique is leveraged to represent the heat map of disease detection, explaining the consistency among clinical doctors and neural network models in the treatment of lung disease from Chest X-ray. The LungMaxVit shows the robust results and generalization in detecting multiple lung lesions and COVID-19 on Chest X-ray images.