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 [18]. 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.

PROBLEM STATEMENT:

This thesis objective is to examine if transfer learning improves a model’s performance when using a small dataset consisting of radiographs. In this case, the model’s goal is to classify healthy, pneumonia and COVID-19. - Can transfer learning from different source domains improve performance for classifying COVID-19? - Does it matter if the basis for the transfer learning is within the same domain or not ?

OBJECTIVE:

The goal of the project is to find out if Deep transfer learning using pneumonia dataset is a valid approach to build a model that classifies patients successfully. This is a highly relevant issue considering the recent outbreak of COVID-19, as stated previously. Furthermore, it can also be useful to find approaches for creating machine learning models with limited training data, for other medical diagnostics problems. As in the medical domain, labelling of data often requires knowledge from skilled professionals which can be tedious.