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Deep Learning-based Techniques in Medical Imaging for COVID-19 Diagnosis: A Survey
Published in Richard Jiang, Li Zhang, Hua-Liang Wei, Danny Crookes, Paul Chazot, Recent Advances in AI-enabled Automated Medical Diagnosis, 2022
Fozia Mehboob, Abdul Rauf, Khalid M. Malik, Richard Jiang, Abdul K.J. Saudagar, Abdullah AlTameem, Mohammed AlKhathami
We explored and analyzed datasets, COVID diagnosis, detection, classification, and experimental findings which can be helpful to identify the future research directions in the domain of automatic detection of COVID-19 disease. It is also reflected that there is a crucial need for comprehensive, public, and diverse COVID-19 datasets. Deep learning is considered an emergent field which can play a fundamental role in COVID-19 detection in future. Till now, several researchers have used deep learning methods, transfer learning, etc. for detection of COVID-19 using X-ray or CT scan images and have got promising results. However, researchers are finding improved and advanced architectures for COVID-19 diagnosis. In this survey paper, we have reviewed these new methods or techniques alongside with implementation of these methods for fair comparison. There is no recently published survey paper which focuses on implementation of existing researchers’ works for diagnosis of COVID-19. Table 1 describes the literature collection and preparation protocol.
A mask-guided attention deep learning model for COVID-19 diagnosis based on an integrated CT scan images database
Published in IISE Transactions on Healthcare Systems Engineering, 2023
Maede Maftouni, Bo Shen, Andrew Chung Chee Law, Niloofar Ayoobi Yazdi, Fahimeh Hadavand, Fereshte Ghiasvand, Zhenyu (James) Kong
Despite the promising learning ability of deep models, the generalization power of the trained network depends on the size, distribution, and quality of the training dataset. Inadequate training datasets can easily lead to over-fitted deep learning models that cannot generalize well on a new dataset. Some COVID-19 datasets have been made publicly available (Afshar et al., 2021; Cohen et al., 2020; He et al., 2020; Jun et al., 2020; MedSeg, 2020; Morozov et al., 2020; Rahimzadeh et al., 2021; Zhao et al., 2020). Zhao et al. (2020) introduced the COVID-CT dataset, which includes 349 COVID-19 CT images from 216 patients and 463 non-COVID-19 (a mix of normal cases and patients with other diseases). Misztal et al. (2020) reported improving classification performance by categorizing negative COVID-19 cases into specific groups and creating the COVID-19 CT Radiograph Image Data Stock dataset with careful data split. Afshar et al. (2021) built an open-sourced dataset named COVID-CT-MD, comprising COVID-19, Normal, and community-acquired pneumonia (CAP) cases. The COVID-CT-MD is accompanied by lobe-level, slice-level, and patient-level labels to aid in developing deep learning methods. Notwithstanding, researchers continue to require more data for deep learning models’ training in order to provide better insights and generalization performance. To this end, our COVID-19 lung CT-scan dataset is curated from seven open-source datasets.