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Deep CNN with LM learning based myocardial ischemia detection in cardiac magnetic resonance images
Insights
A deep convolutional neural network (CNN) with Levenberg-Marquardt (LM) learning effectively screens myocardial ischemia (MI) from cardiac magnetic resonance (CMR) images. This computer-aided detection (CAD) system achieved high accuracy and sensitivity for diagnosing cardiovascular disease.
Area of Science:
- Medical imaging analysis
- Artificial intelligence in healthcare
- Cardiology
Background:
- Cardiovascular disease (CVD) accounts for significant global mortality.
- Myocardial ischemia (MI), a critical CVD, restricts blood flow to the heart.
- Effective computer-aided detection (CAD) systems are needed for early MI diagnosis.
Purpose of the Study:
- To develop and evaluate a deep convolutional neural network (CNN) for myocardial ischemia detection.
- To estimate left ventricle (LV) volume using CNNs for improved MI prediction.
- To compare the efficacy of Levenberg-Marquardt (LM) learning against gradient descent (GD) in CNNs for cardiac analysis.
Main Methods:
- Utilized deep CNN architecture with LM learning for mass screening of myocardial ischemic subjects.
- Performed left ventricle (LV) volume estimation from cardiac magnetic resonance (CMR) images.
- Compared LM-based CNN performance against a GD-based CNN using MICCAI 2009 database samples.
Main Results:
- The LM-based deep CNN achieved 86.39% accuracy and 90% sensitivity in classifying ischemic subjects.
- The LM learning method demonstrated a high Area Under the Curve (AUC) of 0.93.
- Estimated LV volumes from the trained network showed strong correlation with ground truth values.
Conclusions:
- The proposed deep CNN framework with LM learning is an effective CAD system for cardiovascular disorder diagnosis.
- LM learning enhances CNN performance for myocardial ischemia detection and LV volume estimation.
- This approach offers a promising tool for early and accurate diagnosis of cardiac conditions.
Abstract:
Cardiovascular disease (CVD) is a chronic dysfunction caused by deterioration in cardiac physiology. It results in about 31% of mortality worldwide. Among CVDs, myocardial ischemia (MI) leads to restriction in blood supply to heart tissues. There is a need to develop an effective computer aided detection (CAD) system to reduce the fatality. In this work, an attempt is made to perform mass screening of myocardial ischemic subjects and left ventricle (LV) volume estimation from cardiac magnetic resonance (CMR) images using deep convolutional neural network (CNN) with Levenberg-Marquardt (LM) learning. LV volume measurement is an important predictor of myocardial ischemia. The CMR samples used in this analysis are obtained from Medical Image Computing and Computer Assisted Intervention (MICCAI) 2009 database. The results of the proposed model are compared with deep CNN based on gradient descent (GD) learning algorithm. The results show that deep CNN architecture with LM learning classifies ischemic subjects with high accuracy (86.39%) and sensitivity (90%). The LM learning based method gives an AUC of 0.93. The estimated LV volumes obtained from the trained network gives high correlation with the ground truth. Thus the results support that proposed framework of deep CNN architecture with LM learning can be used as an effective CAD system for diagnosis of cardiovascular disorders.
