Textual classifier method applied for medical texts: transfer learning of ResNet on images converted from textual data (Preprint)

Abstract

BACKGROUND In medical text processing, the classification methods with high interpretability are primarily dictionary- or rule-based methods, requiring a lot of labor cost in maintenance. However, in recent years, methods of image classification with higher accuracy than that of humans, have been developed. OBJECTIVE This research attempts to apply the transfer learning of image classification methods to text classification by converting medical text into images for better performance. METHODS This method applies the word embedding method to processed text for feature extraction and then segments it to generate a grayscale image. Then pretrained deep learning method for transfer learning was applied to the grayscale image, and the accuracy of the image classifier was validated by comparing it with that of the deep network transfer learning. RESULTS The transfer learning using pretrained ResNet displayed better validation accuracy. The validation accuracies of ResNet-18, -34, and -50 were 85.7%, 93.0%, and 98.9%, respectively; whereas the accuracy of naïve Bayes was 92.4%, and the pretrained model converged faster than the non-pretrained model. CONCLUSIONS The feasibility of applying the deep learning method of image processing was demonstrated by converting the text format to the image format.