Abstract
The aim of this research work is to improve the reliability of prediction of Virus Pneumonia for chest X-ray images by considering difference aspect of learning. Our method uses convolution neural networks to extract their final states, which became input features for further learning by the SVM. In training the distribution of classes over samples are checked and regulated for similarity of the distribution of classes. This is done by random under-sampling to decrease number of samples that belong to majority class. The collaborate nature of our method covering CNN, checking class distribution and SVM is demonstrated by remarkable results over a benchmark X-ray chest database with very large number of samples.
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Tho, D.X., Anh, D.N. (2021). Deep Learning-Based Imbalanced Data Classification for Chest X-Ray Image Analysis. In: Tran, DT., Jeon, G., Nguyen, T.D.L., Lu, J., Xuan, TD. (eds) Intelligent Systems and Networks . ICISN 2021. Lecture Notes in Networks and Systems, vol 243. Springer, Singapore. https://doi.org/10.1007/978-981-16-2094-2_14
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