TY - GEN
T1 - Application of various CNNs for the Identification of Hypertrophic Cardiomyopathy
AU - James, Jimcymol
AU - Gudigar, Anjan
AU - Raghavendra, U.
AU - Samanth, Jyothi
AU - Rathore, Simran Kumari
AU - Baheti, Aashna
AU - Tiwari, Rhea
AU - Prabhu, Mukund A.
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Hypertrophic Cardiomyopathy (HCM) is an abnormal condition of the heart due to the thickening of heart muscles. Early diagnosis and prognosis of HCM is appreciable as it can be a threaten to life if left untreated. The structural definitions of the heart are easily captured by Echocardiography. An automated system utilizing Echocardiography for the identification of HCM is thus beneficial to the well-being of the society. This study performs a comparative analysis of various Convolutional Neural Networks (CNNs), that are used for extracting the features which are relevant for the classification of HCM. Heart ultrasound images from 35 subjects each of HCM and normal are utilized for the study. Variants of Support Vector Machine (SVM) are used to perform the classification. Further, significant features are analyzed by feature ranking methods. Finally, it is observed from the result that ResNet50 and AlexNet are the best CNN models towards the identification of HCM.
AB - Hypertrophic Cardiomyopathy (HCM) is an abnormal condition of the heart due to the thickening of heart muscles. Early diagnosis and prognosis of HCM is appreciable as it can be a threaten to life if left untreated. The structural definitions of the heart are easily captured by Echocardiography. An automated system utilizing Echocardiography for the identification of HCM is thus beneficial to the well-being of the society. This study performs a comparative analysis of various Convolutional Neural Networks (CNNs), that are used for extracting the features which are relevant for the classification of HCM. Heart ultrasound images from 35 subjects each of HCM and normal are utilized for the study. Variants of Support Vector Machine (SVM) are used to perform the classification. Further, significant features are analyzed by feature ranking methods. Finally, it is observed from the result that ResNet50 and AlexNet are the best CNN models towards the identification of HCM.
UR - https://www.scopus.com/pages/publications/105004556429
UR - https://www.scopus.com/pages/publications/105004556429#tab=citedBy
U2 - 10.1109/AICECS63354.2024.10957517
DO - 10.1109/AICECS63354.2024.10957517
M3 - Conference contribution
AN - SCOPUS:105004556429
T3 - 2024 3rd International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2024
BT - 2024 3rd International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2024
Y2 - 12 December 2024 through 14 December 2024
ER -