  Heart disease is the most common reason for human mortality that causes
almost one-third of deaths throughout the world. Detecting the disease early
increases the chances of survival of the patient and there are several ways a
sign of heart disease can be detected early. This research proposes to convert
cleansed and normalized heart sound into visual mel scale spectrograms and then
using visual domain transfer learning approaches to automatically extract
features and categorize between heart sounds. Some of the previous studies
found that the spectrogram of various types of heart sounds is visually
distinguishable to human eyes, which motivated this study to experiment on
visual domain classification approaches for automated heart sound
classification. It will use convolution neural network-based architectures i.e.
ResNet, MobileNetV2, etc as the automated feature extractors from spectrograms.
These well-accepted models in the image domain showed to learn generalized
feature representations of cardiac sounds collected from different environments
with varying amplitude and noise levels. Model evaluation criteria used were
categorical accuracy, precision, recall, and AUROC as the chosen dataset is
unbalanced. The proposed approach has been implemented on datasets A and B of
the PASCAL heart sound collection and resulted in ~ 90% categorical accuracy
and AUROC of ~0.97 for both sets.
