Detecting COVID-19 Using Transfer Learning
Abstract: COVID-19 is currently an ongoing pandemic and the large demand for testing of the disease has led to insufficient resources in hospitals. In order to increase the efficiency of COVID- 19 detection, computer vision based systems can be used. However, a large set of training data is required for creating an accurate and reliable model, which is currently not feasible to be acquired considering the novelty of the disease. Other models are currently being used within the healthcare sector for classifying various diseases, one such model is for identifying pneumonia cases by using radiographs and it has achieved high enough accuracy to be used on patients . With the background of having limited data for COVID-19 identification, this thesis evaluates the benefit of using transfer learning in order to augment the performance of the COVID-19 detection model. By using pneumonia dataset as a base for feature extraction the goal is to generate a COVID-19 classifier through transfer learning. Using transfer learning, an accuracy of 97% was achieved, compared to the initial accuracy of 32% when transfer learning was not used.
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