  Heart Failure is a major component of healthcare expenditure and a leading
cause of mortality worldwide. Despite higher inter-rater variability,
endomyocardial biopsy (EMB) 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 CNN
(inception-V3 model) and bidirectional LSTM (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.
