Self-attention based bidirectional long short-term memory-convolutional neural network classifier for the prediction of ischemic and non-ischemic cardiomyopathy

Abstract

Heart Failure is a major component of healthcare expenditure and a leading cause of mortality worldwide. Despite higher inter-rater variability, endomyocardial biopsy is still regarded as the standard technique, used to identify the cause (e.g. ischemic or non-ischemic cardiomyopathy, coronary artery disease, myocardial infarction etc) of unexplained heart failure. In this paper, we focus on identifying cardiomyopathy as ischemic or non-ischemic. For this, we propose and implement a new unified architecture comprising convolutional neural network (inception-V3 model) and bidirectional long short-term memory (BiLSTM) with self-attention mechanism to predict the ischemic or non-ischemic to classify cardiomyopathy using histopathological images. The proposed model is based on self-attention that implicitly focuses on the information outputted from the hidden layers of BiLSTM. Through our results, we demonstrate that this framework carries a high learning capacity and is able to improve the classification performance.