U-Net Based Chest X-ray Segmentation with Ensemble Classification for Covid-19 and Pneumonia
Hashara Kumarasinghe, Shammi Kolonne, Chamodi Fernando, Dulani Meedeniya
Abstract
Respiratory diseases have been known to be a main cause of death worldwide. Pneumonia and Covid-19 are two of the dominant diseases. Several deep learning based studies are available in the literature that classifies infection conditions in chest X-ray images. In addition, image segmentation has been also applied to obtain promising results in deep learning approaches. This paper focuses on using a modified version of the U-Net architecture to conduct segmentation on chest X-rays and then use segmented images for classification to assess the impact on the performance. We achieved an Intersection over Union of 93.53% with the proposed modified U-Net architecture and achieved 99.83% accuracy on segmentation aided ensemble classification.