Stroke risk prediction by color Doppler ultrasound of carotid artery-based deep learning using Inception V3 and VGG-16 - Su, Shan-Shan; Li, Li-Ya; Wang, Yi; Li, Yuan-Zhe (Frontiers in Neurology, 2023)
Abstract: Purpose: This paper aims to automatically classify color Doppler images into two categories for stroke risk prediction based on the carotid plaque. The one is high-risk carotid vulnerable plaque, and the second is stable carotid plaque. Method: In this research work, we used a deep learning framework based on transfer learning to classify color Doppler images into two categories: one is high-risk carotid vulnerable plaque, and the second is stable carotid plaque. The data was collected from the Second Affiliation of Fujian Medical University, including normal and vulnerable cases. The 87 patients with risk factors for atherosclerosis in our hospital were selected and divided into the training set and test set in a ratio of 70% and 30%, respectively. We have implemented Inception V3 and VGG- 16 pre-trained models for this classification task. Results: Using the proposed framework, we implemented two transfer deep learning models: Inception V3 and VGG- 16. We achieved the highest accuracy of 93.81% by using fine-tuned and adjusted hyperparameters according to our classification problem. Conclusion: In this research, we classify color Doppler ultrasound images into high-risk carotid vulnerable plaque and the second is stable carotid plaque. We fine-tuned pre-trained deep learning models to classify color Doppler ultrasound images according to our dataset. Our suggested framework will help prevent incorrect diagnoses brought on by low image quality and individual experience, among other factors.