Deep Learning Approach for High Recall Pneumonia Classification with Swin Transformer and L2 Regularization

Pneumonia is one of the lung infections that can be lethal to people. Pneumonia can be diagnosed by conducting an X-ray scan of the human chest. Even though X-ray scanning is straightforward, the process of identifying pneumonia in X-ray data is still difficult to do even by radiologists. Therefore, a computational algorithm is needed that can be used to analyze X-ray image data. The use of Deep Learning (DL) methods for analyzing images has been proven to work well in various cases, especially X-ray data. Based on previous research, the transformer architecture has the advantage of analyzing with a larger receptive area if compared to Convolutional Neural Networks (CNN) which have limited kernel size. In this study, we used the Swin transformer in classifying pneumonia diseases through X-ray images. Through the study conducted, the Swin Transformer Architecture is able to provide a fairly good evaluation metric when compared to the use of convolutional neural networks. The Tiny variant of the Swin Transformer architecture is able to provide evaluation metrics with a precision value of 0.870, recall of 0.995, and F1-Score of 0.928. On the other hand, the use of L2 regularization in stage 3 and stage 4 of the Swin Tiny architecture is able to provide a high recall value of 0.997, but the use of L2 regularization does not provide an increase in precision and F1-Score. In biomedical cases that are only concerned with recall values, the use of L2 regularization on Swin Tiny can provide advantages.