An Explainable AI-Enabled Framework for Interpreting Pulmonary Diseases from Chest Radiographs

Simple Summary Different chest diseases badly affect the human respiration system. The chest radiographs of the lungs are used to classify these diseases. Identifying diseases is essential, but the most important thing is explaining the reason behind classification results. This research provides an explanation of the classification results of different lung pulmonary diseases so that doctors can understand the reason that causes these diseases. This work achieved 97% classification accuracy. This research also evaluated the highlighted regions in the input image, during the explanation of classification results with the manifest file, where the doctor highlighted the same regions with red arrows. The automatic disease explanation and identification will help doctors to diagnose these diseases at a very early stage. Explainable Artificial Intelligence is a key component of artificially intelligent systems that aim to explain the classification results. The classification results explanation is essential for automatic disease diagnosis in healthcare. The human respiration system is badly affected by different chest pulmonary diseases. Automatic classification and explanation can be used to detect these lung diseases. In this paper, we introduced a CNN-based transfer learning-based approach for automatically explaining pulmonary diseases, i.e., edema, tuberculosis, nodules, and pneumonia from chest radiographs. Among these pulmonary diseases, pneumonia, which COVID-19 causes, is deadly; therefore, radiographs of COVID-19 are used for the explanation task. We used the ResNet50 neural network and trained the network on extensive training with the COVID-CT dataset and the COVIDNet dataset. The interpretable model LIME is used for the explanation of classification results. Lime highlights the input image's important features for generating the classification result. We evaluated the explanation using radiologists' highlighted images and identified that our model highlights and explains the same regions. We achieved improved classification results with our fine-tuned model with an accuracy of 93% and 97%, respectively. The analysis of our results indicates that this research not only improves the classification results but also provides an explanation of pulmonary diseases with advanced deep-learning methods. This research would assist radiologists with automatic disease detection and explanations, which are used to make clinical decisions and assist in diagnosing and treating pulmonary diseases in the early stage.