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- W4224880000 abstract "Automatic detection of corona virus infections using CT images is important for rapid examination. The systematic collection of extensive initial training data is difficult. A new conditional generative model called Cycle Generative Adversarial Network (CycleGAN), which is able to record the conditional distribution of individual CT image and synthesis of even higher resolution (512 × 512) and various CT images that exactly match the input conditions. So, it can able to balance the unbalanced dataset by creating synthetic data. The Deep Multiple Instance Learning (DMIL) is applied to train a patch-level classifier, where a Chest CT is viewed as a bag of instances. Then it generates deep instances to the possible infection area to avoid false negative which leads to pandemic. A series of experimental studies show that this algorithm achieves an overall accuracy of 97.4% without augmentation and reaches the accuracy of 98.96% with augmentation using CycleGAN. These advantages make this algorithm as an effective tool to screen the COVID-19 waves." @default.
- W4224880000 created "2022-04-27" @default.
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- W4224880000 date "2022-03-25" @default.
- W4224880000 modified "2023-10-01" @default.
- W4224880000 title "Automated COVID-19 diagnosis using Deep Multiple Instance Learning with CycleGAN" @default.
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- W4224880000 doi "https://doi.org/10.1109/icstsn53084.2022.9761334" @default.
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