An adaptive data processing framework for cost- effective covid-19 and pneumonia detection - Kin, Wai Lee; Ka, Renee Yin Chin (Institute of Electrical and Electronics Engineers, 2021)
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.