  Artificial Intelligence (AI) has made leapfrogs in development across all the
industrial sectors especially when deep learning has been introduced. Deep
learning helps to learn the behaviour of an entity through methods of
recognising and interpreting patterns. Despite its limitless potential, the
mystery is how deep learning algorithms make a decision in the first place.
Explainable AI (XAI) is the key to unlocking AI and the black-box for deep
learning. XAI is an AI model that is programmed to explain its goals, logic,
and decision making so that the end users can understand. The end users can be
domain experts, regulatory agencies, managers and executive board members, data
scientists, users that use AI, with or without awareness, or someone who is
affected by the decisions of an AI model. Chest CT has emerged as a valuable
tool for the clinical diagnostic and treatment management of the lung diseases
associated with COVID-19. AI can support rapid evaluation of CT scans to
differentiate COVID-19 findings from other lung diseases. However, how these AI
tools or deep learning algorithms reach such a decision and which are the most
influential features derived from these neural networks with typically deep
layers are not clear. The aim of this study is to propose and develop XAI
strategies for COVID-19 classification models with an investigation of
comparison. The results demonstrate promising quantification and qualitative
visualisations that can further enhance the clinician's understanding and
decision making with more granular information from the results given by the
learned XAI models.
