Classification of Heart Disease Using MFO Based Neural Network on MRI Images
Authors: K. Kalaivani , N. Uma Maheswari , R. Venkatesh 
Published: February 2021

Background:
Cardiovascular Disease (CVD) is one of the primary diseases that causes
death every year. An approximation of roughly about 17.5 million people dies due to CVD, signifying
about 31% of global deaths. Based on the statistics, every 34 seconds, a person dies due to
heart disease. Various classification algorithms have been developed and utilized as classifiers to
support doctors who are ineffectually diagnosed with heart disease.

Aims:
The main aim of this work is to improve the performance of the heart disease approach using
image processing algorithm. To improve the effectiveness and efficiency of classification performance
for heart disease diagnosis, an optimized neural network was proposed based on the feature
extraction and selection approach for handling features.

Objective:
The objective of this investigation is to diagnose heart diseases using feature extraction,
reduction based classification and image processing methods. The proposed model comprises of
two subsets: Feature extraction using gray scale properties and Moth flame optimization (MFO) for
effectual feature selection, followed by a classification technique using Generalized Regression
Neural Network. The first system includes three stages: (i) Pre-processing of the dataset (ii) feature
extraction (iii) performing MFO for efficient selection. In the second method, GRNN is proposed.
The heart data set obtained from ACDC Challenge, was utilized for performing the computation.

Methods:
The image obtained from the MRI-scanner is in the NIfTI image format. The pre-process
step used in this is to convert the image type from INT16 to uint8 to improve the quality of image
viewing and for feature extraction process. In this phase, the texture properties from the pre-processed
image are calculated and the value is in the numeric format. These values are the feature attributes
of the dataset. The feature attributes of the image are given as input for the moth flame optimization
process and output is the feature selected from the optimization process. The whole process
is performed on the feature attributes of the image by determining the optimal feature for the
classifier by reducing its error rate. The optimal feature from the moth flame optimization is used
for training and testing the network. The classifier used in this approach is a single neural network
classifier with a regression nature. Due to the regression property, the network is well trained with
the feature. The Generalized regression neural network is used for classifying heart diseases.

Results:
The proposed method achieves the accuracy of 96.23%, sensitivity of 95.41% and specificity
of 96.75%. These values are calculated based on the confusion matrix of the classifier.

Conclusion:
In this, the feature extraction using the gray scale properties plays an important role to
determine the feature attributes from the MRI heart images. The Moth flame optimization able to
produce an accuracy of 97.23% using GRNN for classification with minimum single attribute
mean of the image. It also outperformed the other methods, either the feature extraction based classification
or the feature reduction based classification.