Quantum Preprocessing for Deep Convolutional Neural Networks in Atherosclerosis Detection
Siddhartha Bhattacharyya, Mario Köppen, Elizabeth Behrman +1 more
Coronary artery disease is the leading cause of mortality rate worldwide. This condition is caused by an abnormal narrowing or occlusion of the artery lumen, known as atherosclerosis (a specific type of stenosis). Currently, many imaging techniques have been tested; nevertheless, conventional X-ray Coronary Angiography (XCA) remains the gold standard for coronary artery disease diagnosis, such as atherosclerosis. This study presents a Hybrid Quantum-Convolutional Neural Network to detect coronary artery atherosclerosis in XCA images automatically. It employs a Quantum Convolutional Layer (QCL) that resembles a typical Convolutional Layer. However, unlike the convolution operation where a classical convolutional filter is applied, a QCL transforms the input using a quantum circuit. This QCL generates a multichannel image from a single channel XCA that feeds a traditional Convolutional Neural Network (CNN). The CNN was trained and optimized using two different architectures previously employed for atherosclerosis detection, viz. a DenseNet-based and a VGG-based architectures. Additionally, a comparison of Stochastic Gradient Descent (SGD) and SGD with Momentum (SGDM) were conducted. These architectures were analyzed and compared using a real dataset composed of 250 real XCA images (split into 125 images for testing and 125 for training) regarding their training without the QCL preprocessing and SGDM. The results showed that the CNNs trained with the outcome of the QCL surpasses the baseline CNNs performance. Herein, the best atherosclerosis detection reached an accuracy of 83.36%, while in other metrics such as precision, sensitivity, specificity, and F1-score, achieved an 81.52%, 86.67%, 83.99%, and 80.00%, respectively.
CRC Press eBooks•Mar 31, 2022•0 citations