COVID-19 Detection Using Transfer Learning Approach from Computed Tomography Images

Our main goal in this study is to propose a transfer learning based method for COVID-19 detection from Computed Tomography (CT) images. The transfer learning model used for the task is a pretrained Xception model. Both model architecture and pre-trained weights on ImageNet were used. The resulting modified model was trained with 128 batch size and 224x224, 3 channeled input images, converted from original 512x512, grayscale images. The dataset used is a the COV19-CT-DB. Labels in the dataset include COVID-19 cases and Non-COVID-19 cases for COVID-1919 detection. Firstly, a accuracy and loss on the validation partition of the dataset as well as precision recall and macro F1 score were used to measure the performance of the proposed method. The resulting Macro F1 score on the validation set exceeded the baseline model.