DENTIFICATION OF CORONARY ARTERY DISEASE THROUGH IRIS BY USING CONVOLUTION NEURAL NETWORKS
Now-a-days, coronary heart disease is one of the deadliest diseases in the world. An unfavorable lifestyle,
lack of physical activity, and consuming tobacco are the causes of coronary heart disease aside from genetic
inheritance. Sometimes the patient does not know whether he has abnormalities in heart function or not.
Therefore, this study proposes a system that can detect heart abnormalities through the iris, known as the
Iridology method. The system is designed automatically in the iris detection to the classification results.
Feature extraction using five characteristics is applied to the Gray Level Co-occurrence Matrix (GLCM)
method. The classification process uses the Convolutional Neural Networks (CNN) with linear kernel, to
obtain the best accuracy in the system. From the system simulation results, the use of CNN classification of
iris conditions with an accuracy rate of 98-99%. This study has succeeded in detecting heart conditions
through the iris by dividing the iris into normal iris and abnormal iris.
RESULTS AND ANALYSIS
In this study, system training was carried out using 40 normal iris data and 40 abnormal iris data. Normal iris data is the iris of people who have no history of heart disease; on the contrary for abnormal iris data is the iris of people who have heart disease. In this study, system training was carried out using 40 normal iris data and 40 abnormal iris data. Normal iris data is the iris of people who have no history of heart disease; on the contrary abnormal iris data is the iris of people who have heart disease. The training data uses linear kernel variations. Iris data in training can be separated according to normal (red) and abnormal (blue) classes. The results of linear kernel training separate the data into each class with an even distribution of data. The results of the training can train the machine learning model, which can help in the classification of test data and affect the level of recognition accuracy.
This study has proposed a new method to determine the condition of the heart through the iris using the CNN classification.
The use of GLCM characteristics as feature extraction has an essential role in the classification process.
The main contribution in this study is not only limited to determining heart health conditions through the iris but also contributes to the automatic processing of the iris with CHT.
The proposed system for determining the heart condition automatically is to optimize the classification by using angles 0° and 90° on GLCM with CNN classification to obtain a high level of accuracy. In ongoing research, the iris database can be added to improve the classification to make it more accurate
Industrial Engineering Journal
Volume : 52, Issue 4, April : 2023
UGC CARE Group-1, 2375
IDENTIFICATION OF CORONARY ARTERY DISEASE THROUGH IRIS BY USING
CONVOLUTION NEURAL NETWORKS
K.Chandra Rao , Asst.Professor , ECE Department , Amrita Sai Institute of Science and Technology ,
Paritala , Kanchikacherla, NTR District , Andhra Pradesh , India.
K. Sowmya Sri, P. Niharika, K. Satyavani, K. Manasa, UG Students , ECE Department , Amrita Sai
Institute of Science and Technology , Paritala , Kanchikacherla, NTR District , Andhra Pradesh , India
Download Full Abstract PDF