Lungs Disease Classification Using an Self Attention Based Neural Network: A Local Agnostic Explainable Approach

Understanding the future potential candidates for lung disease classification poses a great challenge for physicians. If they can understand the future lungs disease patients from their Chest X-ray images, it would help them in proper design of medication strategies. It helps them to reduce their overburdening during physical examination of their patients. Physician often face difficulty in accurately finding the proper chest disease. However, an explainable neural network enabled architecture can help in better diagnosis of lung diseases. Hence, this study proposes an attention-based Convolutional Neural Network and consequently followed by a local agnostic explanation method for better understanding what made the artificial intelligence model to predict the image belongs to either Bacterial Tuberculosis, Pneumonia, Normal, Viral Pneumonia. We achieved a BCA of 84% on the testing set. Moreover, an explainable AI based local agnostic framework is used to understand which portion of the lungs make a class distinguishable.