Evaluation Algorithm for the Effectiveness of Stroke Rehabilitation Treatment Using Cross-Modal Deep Learning

Abstract

It is important to study the evaluation algorithm for the stroke rehabilitation treatment effect to make accurate evaluation and optimize the stroke disease treatment plan according to the evaluation results. To address the problems of poor restoration effect of positron emission tomography (PET) image and recognition restoration effect of evaluation data and so on. In the paper, we propose a stroke rehabilitation treatment effect evaluation algorithm based on cross-modal deep learning. Magnetic resonance images (MRI) and PET of stroke patients were collected as evaluation data to construct a multimodal evaluation dataset, and the data were divided into positive samples and negative samples. According to the mapping relationship between MRI and PET, three-dimensional cyclic adversarial is used to generate the neural network model to recover the missing PET data. Using the cross-modal depth learning network model, the RGB image, depth image, gray image, and normal images of MRI and PET are taken as the feature images and the multifeature fusion method is used to fuse the feature images, output the recognition results of MRI and PET, and evaluate the effect of stroke rehabilitation treatment according to the recognition results. The results show that the proposed algorithm can accurately restore PET images, the evaluation data recognition effect is good, and the evaluation data recognition accuracy is higher than 95%. The evaluation accuracy of stroke rehabilitation treatment effect is high, the evaluation time varies between 0.56 s and 0.91 s, and the practical application effect is good.