Interpreting the predictions of Deep Network build to identify early
detection of Covid-19 in X-Ray Images
Abstract
AI is proven technology which is currently serving many different
industries. Weather forecasting, recommendation system, autonomous car
are few of the examples where AI driven solutions are successfully used.
Availability of intensive computing makes it possible to design and
develop highly complicated deep learning architecture which is desire to
reach human level of accuracy. Because of this reason it become possible
to utilize AI technology in healthcare industry where accuracy is utmost
important. Healthcare industry generates various types of Electronic
Health Records (EHR) like patient medical history, hospital
administration data, biological data, radiological data etc. These type
of EHR data can have huge potential in diagnosis various diseases and
potentially avoiding any critical risk. AI is also contributing
significantly on drug discovery, understanding genetic disorder, cancer
detection and many more. All such complex use case needs a complex
AI-Deep Neural Network (DNN). Due to its complex architecture these DNN
models considered as black box and it became difficult to explain the
outcome of such models. Entrust on such solutions considered as a major
concern area. Various techniques have been evolved that try to explain
the reasoning behind the outcome of such DNN. On this paper two such
explanation techniques LIME and LRP is used on explain the prediction
made on custom build CNN model. Custom CNN model is trained on Covid-19
patients X-RAY images. The main objective of this paper is to present
the explanation difference made by LIME and LRP. Later domain experts
can analyze the model predictions and facilities in improving the
explanation techniques.