Detection of Atherosclerosis Using Deep Learning

Atherosclerosis is one of the major causes of cardiovascular disease, requiring early detection and intervention. Deep learning techniques, particularly transfer learning, offer potential avenues for improving the diagnosis and management of atherosclerosis. In this paper, a transfer learning approach is proposed to enhance the detection of atherosclerotic plaque by adapting pre-trained Convolutional Neural Networks (CNNs). By fine-tuning these models on a dataset of medical images, the study aims to leverage learned representations to improve detection accuracy and efficiency. Additionally, data augmentation is used to enhance model robustness and address data scarcity and class imbalance issues. The findings from our experiments indicate that the ResNet-50 model outperformed others in terms of Recall at 1.0, followed by Inception-v3 at 0.941. In classification accuracy, ResNet-50 achieved 93%, followed by the Inception-v3 model at 86%. Similarly, in AUC-ROC performance, the ResNet-50 model attained the highest score of 0.99, with the Inception-v3 model following closely at 0.966. Our results demonstrate that transfer learning significantly improves the accuracy, sensitivity, and specificity of the detection of atherosclerotic plaque, showcasing its potential as a valuable tool for the detection of atherosclerosis.