An adaptive data processing framework for cost- effective covid-19 and pneumonia detection

• Main Authors: Kin, Wai Lee, Ka, Renee Yin Chin
• Format: Proceedings
• Language: English; English
• Published: Institute of Electrical and Electronics Engineers 2021
• Subjects: QA1-43 General; RC581-951 Specialties of internal medicine
• Online Access: https://eprints.ums.edu.my/id/eprint/32426/1/An%20adaptive%20data%20processing%20framework%20for%20cost-%20effective%20covid-19%20and%20pneumonia%20detection.ABSTRACT.pdf; https://eprints.ums.edu.my/id/eprint/32426/2/An%20adaptive%20data%20processing%20framework%20for%20cost%20effective%20Covid-19%20and%20pneumonia%20detection.pdf; https://eprints.ums.edu.my/id/eprint/32426/; https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9576805
• Institution: Universiti Malaysia Sabah

Medical imaging modalities have been showing great potentials for faster and efficient disease transmission control and containment. In the paper, we propose a costeffective COVID-19 and pneumonia detection framework using CT scans acquired from several hospitals. To this end, we incorporate a novel data processing framework that utilizes 3D and 2D CT scans to diversify the trainable inputs in a resource-limited setting. Moreover, we empirically demonstrate the significance of several data processing schemes for our COVID-19 and pneumonia detection network. Experiment results show that our proposed pneumonia detection network is comparable to other pneumonia detection tasks integrated with imaging modalities, with 93% mean AUC and 85.22% mean accuracy scores on generalized datasets. Additionally, our proposed data processing framework can be easily adapted to other applications of CT modality, especially for cost-effective and resource-limited scenarios, such as breast cancer detection, pulmonary nodules diagnosis, etc.