Few-Shot Learning based on Residual Neural Networks for X-ray Image Classification

Currently, deep learning is widely used in the field of medicine, which in turn includes radiology. This paper considers the problem of the classification of X-ray images and the lack of images of specific classes. The classes included COVID-19 and Normal X-ray scans. To solve the problems, we propose few-shot learning that is based on different Residual Convolutional Neural Network models with different complexities. The method is designed for the datasets that have small amount of samples of a specific class and a larger amount of instances of another class. The utilization of few-shot learning can solve the issues of the balance of X-ray datasets. The Residual Convolutional Neural Network models we used are as follows: ResNet-50, ResNet-101, and ResNet-152. The architectures had been used to extract the features from the images that were used later. The latter model has the highest complexity, while the former has the lowest complexity, respectively. The obtained results include the highest accuracy of 97.7% for 10 shots of COVID-19 positive X-ray images. The accuracy was achieved using ResNet-101 model. The highest result for ResNet-152 model was 95.6 %. However, on average, the model achieved the highest accuracy. ResNet-50 model provided the least accurate results, however, it is less complex which provides faster performance. One can also notice that with the higher number of COVID-19 positive shots that were used for training, the accuracy also gets higher. To provide transparency to our solution, we furthermore created t-distributed stochastic neighbor embedding visualization. This showed us that the system could separate the two classes into two distinct clusters. Overall, the results imply the efficiency of the solution that was proposed in the study.