Objective

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Cerebral ischemic stroke is the most common type of cerebral stroke

which is characterized by high morbidity

mortality and disability. The lack of obvious symptoms before the onset of the disease

the rapid onset of the disease

and the narrow time window for thrombolytic therapy have led to it being a high-risk disease in clinical practice. Although initial progress has been made in cerebral stroke prevention and treatment

it remains a significant cause of disability or death in adults. According to the survey

approximately 75% of stroke patients have varying degrees of functional impairment and loss of work

causing a heavy burden on families and society. With the accelerated aging and urbanization of society

the prevalence of unhealthy lifestyles among the population and the widespread exposure to cerebrovascular disease risk factors

in the disease burden of stroke has greatly increased

with a trend of rapid growth in low-income groups

marked gender and geographical differences and youthfulness. Therefore

effective ways to reduce disability and mortality rates should be developed. The early diagnosis of cerebral stroke is important. Many methods can be used to diagnose cerebral stroke in modern medicine

but the processes are relatively complex. In addition

some tests have certain drawbacks

and the presence of the disease is hard to detect in the early stages of illness

thus requiring advanced equipment and experienced clinicians. How to improve the accuracy of early diagnosis of cerebral stroke has become an important research hotspot for medical aid diagnosis. The characteristics and advantages of traditional Chinese medicine (TCM) are essential in the contemporary medical system of diseases

especially the inspection diagnosis of TCM

which is the most important in TCM diagnosis. Chinese medicine diagnosis is an objective and accurate empirical medicine

which has gradually formed and developed in long-term medical practice and clinically proven

with extremely rich connotations. Based on the basic principles of Chinese medicine diagnosis (the inspection diagnosis of TCM)

and diagnosis can be improved by applying modern scientific knowledge and methods in practice. This method not only provides strong evidence for early diagnosis and treatment

but also has extremely important practical significance in saving medical resources

reducing the medical burden on patients and alleviating the harm caused by cerebral stroke disease.

Method

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First

feature extraction is performed on the images of the patient's face and hands. The color features are easily affected by light

and the chroma component in ${\rm{YCbCr}}$ color space is used to reduce the effect of luminance. The most important of the texture features are the features of texture length

depth and thickness in the images

and the gray level co-generation matrix (GLCM) was used to extract the image texture features effectively. Then

the higher-order spatial dimensional features further learned from the original image and the attentional features are learned from the different features by designing a reasonable dual Transformer joint classification model. Different transformer modules were cascaded

and multi-layer perception was used for image classification. This method not only considers color and texture features in the image

but also analyzes the spatial features of the image. Based on the differences arising from successive changes in color and texture between different regions in an image

this paper uses transformer to extract the attention features between different regions to improve the performance of the diagnostic model. In addition

the detection model is trained end-to-end. During the training process

the batch size is set to 4

the learning rate is set to 1E-5 and the maximum number of cycles is set to 100. The experiment uses NVIDIA TITAN XP GPU

and the data set was divided into five groups equally for five cross-validations. Finally

the average accuracy of all cross-validated results was taken as the final result of the experiment.

Result

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When detecting cerebral ischemic stroke

the models with color features (${\rm{YCbCr}}$) and texture features (GLCM) extracted separately achieved accuracies of 79.40% and 80.46% on the dataset

while the model with the fusion of color and texture features achieved an accuracy of 83.53% on the dataset

which was significantly better than the model without feature fusion. Color features and texture features can effectively improve the classification accuracy in classification by using a transformer model

and feature fusion can make the model further improve the detection accuracy. Under the premise of fusion of color and texture features

the accuracy of model classification using a transformer module has dropped by approximately 2%. This finding shows that features from different parts play different roles in the final detection

and the gaps between the same features from different parts can easily disappear in the process of feature fusion into one transformer module. The dual transformer joint classification model uses color

texture

spatial and attention features

and the combination of these features can effectively improve the performance of the model. In addition

the average accuracy of the proposed model on the dataset in this paper outperforms the experimental results of related classification models.

Conclusion

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In this paper

we proposed an end-to-end joint classification detection method based on the dual Transformer module. High-quality data were acquired using YCbCr color space and GLCM to accelerate the convergence process of the model. In addition

we extracted feature information from the patient's face and hand images. More importantly

the model learning capability was enhanced

and the model performance was improved using a self-attentive mechanism to learn the association between features and assign weights. The proposed model has a good diagnostic effect

and the automatic assisted diagnosis reduced the influence of subjective factors

which is valuable in the study of cerebral ischemic stroke auxiliary diagnosis

provides a reference for clinicians to make decisions on cerebral ischemic stroke disease diagnosis and provides a new method for patients to conduct effective self-screening.