COVID-19 Detection Using Slices Processing Techniques and a Modified Xception Classifier from Computed Tomography Images
This incremental improvement addresses COVID-19 diagnosis for medical imaging, offering a slightly enhanced solution over existing approaches.
The paper tackled COVID-19 detection from CT images by removing extreme slices and cropping lung areas, then using a modified Xception model, achieving higher validation accuracy and macro F1 scores compared to previous methods on the COV19-CT database.
This paper extends our previous method for COVID-19 diagnosis, proposing an enhanced solution for detecting COVID-19 from computed tomography (CT) images. To decrease model misclassifications, two key steps of image processing were employed. Firstly, the uppermost and lowermost slices were removed, preserving sixty percent of each patient's slices. Secondly, all slices underwent manual cropping to emphasize the lung areas. Subsequently, resized CT scans (224 by 224) were input into an Xception transfer learning model. Leveraging Xception's architecture and pre-trained weights, the modified model achieved binary classification. Promising results on the COV19-CT database showcased higher validation accuracy and macro F1 score at both the slice and patient levels compared to our previous solution and alternatives on the same dataset.