Detecting Coronary Artery Disease from Computed Tomography Images Using a Deep Learning Technique

Abstract

In recent times, coronary artery disease (CAD) has become one of the leading causes of morbidity and mortality across the globe. Diagnosing the presence and severity of CAD in individuals is essential for choosing the best course of treatment. Presently, computed tomography (CT) provides high spatial resolution images of the heart and coronary arteries in a short period. On the other hand, there are many challenges in analyzing cardiac CT scans for signs of CAD. Research studies apply machine learning (ML) for high accuracy and consistent performance to overcome the limitations. It allows excellent visualization of the coronary arteries with high spatial resolution. Convolutional neural networks (CNN) are widely applied in medical image processing to identify diseases. However, there is a demand for efficient feature extraction to enhance the performance of ML techniques. The feature extraction process is one of the factors in improving ML techniques’ efficiency. Thus, the study intends to develop a method to detect CAD from CT angiography images. It proposes a feature extraction method and a CNN model for detecting the CAD in minimum time with optimal accuracy. Two datasets are utilized to evaluate the performance of the proposed model. The present work is unique in applying a feature extraction model with CNN for CAD detection. The experimental analysis shows that the proposed method achieves 99.2% and 98.73% prediction accuracy, with F1 scores of 98.95 and 98.82 for benchmark datasets. In addition, the outcome suggests that the proposed CNN model achieves the area under the receiver operating characteristic and precision-recall curve of 0.92 and 0.96, 0.91 and 0.90 for datasets 1 and 2, respectively. The findings highlight that the performance of the proposed feature extraction and CNN model is superior to the existing models.

Developing a Deep-Learning-Based Coronary Artery Disease Detection Technique Using Computer Tomography Images

“…In addition, the Alothman A.F. et al model [ 4 ], Papandrianos N et al model [ 7 ], Moon, J.H. et al model [ 8 ], and Banerjee, R. et al model [ 9 ] are used for performance comparison.…”

“…In contrast, the Alothman A.F. et al [ 4 ] model, Papandrianos N. et al [ 7 ] model, Moon, J.H. et al [ 8 ] model, and Banerjee R. et al [ 9 ] model consumed a learning rate of 1 × 10 −4 , 1 × 10 −3 , 1 × 10 −3 , and 1 × 10 −3 , respectively.…”

“…Alothman A.F. et al [ 4 ] suggested a feature extraction strategy and a CNN model to identify CAD in the shortest amount of time while maintaining the highest level of accuracy. The effectiveness of the suggested model is examined using two datasets.…”

“…Alothman A. F. et al [ 4 ] developed a CAD detection model using the DL technique. They employed the CNN model for classifying the CCTA images.…”

Editorial on Special Issue “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”

“…AlOthman et al [ 42 ] proposed a novel feature extraction technique with minimal computational overload to detect and assess the severity of coronary artery disease (CAD) using CT images. The authors used the enhanced features from the accelerated segment test (FAST) to reduce the dimensions of the features extracted from a CNN model.…”