The purpose of this study is to devise a Computer Aided Diagnosis (CAD) system that is able to detect COVID-19 abnormalities from chest radio-graphs with increased efficiency and accuracy. We investigate a novel deep learning based ensemble model to classify the category of pneumonia from chest X-ray images. We use a labeled image dataset provided by Society for Imaging Informatics in Medicine for a kaggle competition that contains chest radio-graphs. And the task of our proposed CAD is to categorize between negative for pneumonia or typical, indeterminate, atypical for COVID-19. The training set (with labels publicly available) of this dataset contains 6334 images belonging to 4 classes. Furthermore, we experiment on the efficacy of our proposed ensemble method. Accordingly, we perform a ablation study to confirm that our proposed pipeline drives the classification accuracy higher and also compare our ensemble technique with the existing ones quantitatively and qualitatively.
We evaluate the use of TernausNet V2, a pre-trained VGG-16 U-net for segmentation of Green Fluorescent Protein (GFP) stained stem cells from giga pixel fluorescence microscopy images. Fluorescence microscopy is a difficult modality for automated stem cell segmentation algorithms due to high noise and low contrast. As such segmentation algorithms for cell counting and tracking typically yield more consistent results in other imaging modalities such as Phase Contrast (PC) microscopy due to greater ability to distinguish between foreground and background. Recent methods have shown that U-net based models can achieve state-of-the-art segmentation performance of GFP microscopy, although all available methods continue to overly segment the protein features and have difficulty capturing the entirety the cell. We investigate the use of TernausNet, a VGG-16 based U-Net architecture that was pre-trained from ImageNet and show that it is able to improve the accuracy of GFP stem cell segmentation on gigascale NIST fluorescence microscopy images in comparison to a baseline U-net model. Quantitative results show that the proposed TernausNet V2 architecture model is able to better distinguish the entire region of the cell and reduce overly segmenting proteins as compared to U-net. TernausNet achieved greater accuracy with ROC AUC of 0.956 and F1-Score of 0.810 as compared to the baseline U-net with AUC 0.936 and F1-Score 0.775. Therefore, we suggest that the TernausNet V2 architecture with transfer learning improves the performance of stem-cell segmentation is able to outperform U-net models for the segmentation of giga pixel GFP stained fluorescence microscopy images.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.