TY - GEN
T1 - CNN Modified Approach for Classifying Cardiomegaly Disease Based on CXR Image
AU - Heranurweni, Sri
AU - Mardiyanto, Ronny
AU - Yuniarno, Eko Mulyanto
AU - Nugroho, Andi Kurniawan
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This research focuses on the classification of cardiomegaly by developing and evaluating a CNN model based on CXR images. This research is motivated by the high number of people suffering from heart disease and one of them is the prevalence of cardiomegaly in Indonesia, which constitutes a serious public health problem. The aim of this study is to increase the accuracy of cardiomegaly classification and compare the results with other existing methods. The CNN modification method used is the addition of a convolution layer to add features to the cardiomegaly CXR which are almost the same as the normal heart CXR. The processed data set is then divided into training and testing sets, and data augmentation is performed for both sets. The proposed deep learning models, including Modified CNN, Xception, and Inception ResNetV2, are built and trained using the RMSprop optimizer observing its impact on model performance. The results show that the modified CNN model provides the best performance with an accuracy of 72.64%, higher than previous research using the Inception V3 and SVM models. Performance evaluation is done using metrics such as accuracy, precision, recall, and F1 value. The research results concluded that the modified CNN model could improve the accuracy of cardiomegaly classification, thus making a significant contribution to the development of better cardiomegaly detection methods. This study also highlights the importance of using deep learning models to analyze accurate disease diagnosis.
AB - This research focuses on the classification of cardiomegaly by developing and evaluating a CNN model based on CXR images. This research is motivated by the high number of people suffering from heart disease and one of them is the prevalence of cardiomegaly in Indonesia, which constitutes a serious public health problem. The aim of this study is to increase the accuracy of cardiomegaly classification and compare the results with other existing methods. The CNN modification method used is the addition of a convolution layer to add features to the cardiomegaly CXR which are almost the same as the normal heart CXR. The processed data set is then divided into training and testing sets, and data augmentation is performed for both sets. The proposed deep learning models, including Modified CNN, Xception, and Inception ResNetV2, are built and trained using the RMSprop optimizer observing its impact on model performance. The results show that the modified CNN model provides the best performance with an accuracy of 72.64%, higher than previous research using the Inception V3 and SVM models. Performance evaluation is done using metrics such as accuracy, precision, recall, and F1 value. The research results concluded that the modified CNN model could improve the accuracy of cardiomegaly classification, thus making a significant contribution to the development of better cardiomegaly detection methods. This study also highlights the importance of using deep learning models to analyze accurate disease diagnosis.
KW - CNN
KW - Cardiomegaly
KW - Chest X-ray images
KW - Classification accuracy
KW - Performance evaluation
UR - http://www.scopus.com/inward/record.url?scp=85204997943&partnerID=8YFLogxK
U2 - 10.1109/IES63037.2024.10665768
DO - 10.1109/IES63037.2024.10665768
M3 - Conference contribution
AN - SCOPUS:85204997943
T3 - 2024 International Electronics Symposium: Shaping the Future: Society 5.0 and Beyond, IES 2024 - Proceeding
SP - 575
EP - 580
BT - 2024 International Electronics Symposium
A2 - Yunanto, Andhik Ampuh
A2 - Ramadhani, Afifah Dwi
A2 - Prayogi, Yanuar Risah
A2 - Putra, Putu Agus Mahadi
A2 - Rahmawati, Weny Mistarika
A2 - Rusli, Muhammad Rizani
A2 - Humaira, Fitrah Maharani
A2 - Nadziroh, Faridatun
A2 - Sa'adah, Nihayatus
A2 - Muna, Nailul
A2 - Rizki, Aris Bahari
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th International Electronics Symposium, IES 2024
Y2 - 6 August 2024 through 8 August 2024
ER -