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About Unbalanced and Small Sample Deep Learning For COVID X-Ray Classification
The document discusses developing a deep learning model for COVID-19 chest X-ray classification using limited and imbalanced data. It proposes using data sampling techniques, specialized loss functions, and an attention-based convolutional neural network to achieve high recall while maintaining accuracy. Transfer learning is used to pretrain the model on larger chest X-ray datasets before fine-tuning on a smaller COVID-19 dataset. Experimental results show the approach achieves 96% recall and 92.5% accuracy for COVID-19 classification.
Detailed Information
Author: | ['S. SreeRamaVamsidhar'] |
---|---|
Publication Year: | 2021 |
Pages: | 13 |
Language: | English |
Format: | |
Price: | FREE |
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