Pairwise meta learning pipeline: classifying COVID-19 abnormalities on chest radio-graphs

Sourajit Saha; Yaacov Yesha; Yelena Yesha; Aryya Gangopadhyay; David Chapman; Michael Morris; Babak Saboury; Phuong Nguyen

doi:10.1117/12.2613235

4 April 2022 Pairwise meta learning pipeline: classifying COVID-19 abnormalities on chest radio-graphs

Sourajit Saha, Yaacov Yesha, Yelena Yesha, Aryya Gangopadhyay, David Chapman, Michael Morris, Babak Saboury, Phuong Nguyen

Author Affiliations +

Proceedings Volume PC12033, Medical Imaging 2022: Computer-Aided Diagnosis; PC1203302 (2022) https://doi.org/10.1117/12.2613235
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Sourajit Saha, Yaacov Yesha, Yelena Yesha, Aryya Gangopadhyay, David Chapman, Michael Morris, Babak Saboury, and Phuong Nguyen "Pairwise meta learning pipeline: classifying COVID-19 abnormalities on chest radio-graphs", Proc. SPIE PC12033, Medical Imaging 2022: Computer-Aided Diagnosis, PC1203302 (4 April 2022); https://doi.org/10.1117/12.2613235

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