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Heart Failure is a major component of healthcare expenditure and a leadingcause of mortality worldwide. Despite higher inter-rater variability,endomyocardial biopsy (EMB) is still regarded as the standard technique, usedto identify the cause (e.g. ischemic or non-ischemic cardiomyopathy, coronaryartery disease, myocardial infarction etc.) of unexplained heart failure. Inthis paper, we focus on identifying cardiomyopathy as ischemic or non-ischemic.For this, we propose and implement a new unified architecture comprising CNN(inception-V3 model) and bidirectional LSTM (BiLSTM) with self-attentionmechanism to predict the ischemic or non-ischemic to classify cardiomyopathyusing histopathological images. The proposed model is based on self-attentionthat implicitly focuses on the information outputted from the hidden layers ofBiLSTM. Through our results we demonstrate that this framework carries a highlearning capacity and is able to improve the classification performance.

Self-attention based BiLSTM-CNN classifier for the prediction of ischemic and non-ischemic cardiomyopathy