EP-CapsNet: Extending Capsule Network with Inception Module for Electrophoresis Binary Classification

Abstract

Electrophoresis (EP) test separates protein components based on their density. Patterns exhibited by this test mostly show very close approximation, making it difficult to examine test results within a short amount of time as it has many variations of patterns and requires a significant amount of knowledge to discern them accurately. To help clinical examiners save time and produce consistent results, a new deep-learning model optimized for EP graphic images was developed. Extending recent work on capsule network, which is a state-of-the-art deep learning model, this study was carried out to develop a best-performing model in classifying abnormal and normal electrophoresis patterns. Instead of extracting features from the image, we used the whole slide image as an input to the classifier. This study used 39,484 electrophoresis 2D graph images and utilized capsule network as the foundation of the deep learning architecture to learn the images without data augmentation. The formulated models were compared for a multitude of performance metrics including accuracy, sensitivity, and specificity. Overall, the study results show that our proposed architecture EP-CapsNet, which combines capsule network with Google's inception module, is the best performing model, outperforming the baseline and alternative models in almost all comparisons.