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

TL;DR

This paper introduces a novel self-attention based BiLSTM-CNN model that effectively classifies ischemic versus non-ischemic cardiomyopathy from histopathological images, improving diagnostic accuracy.

Contribution

It presents a new unified deep learning architecture combining CNN, BiLSTM, and self-attention for cardiomyopathy classification from histopathological data.

Findings

High classification accuracy achieved

Self-attention improves focus on relevant features

Model outperforms existing methods

Abstract

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.