FuseLGNet: Fusion of Local and Global Information for Detection of Parkinson’s Disease

In the past few years, the assessment of Parkinson’s disease (PD) has mainly been based on the clinician’s examination, the patient’s medical history, and self-report. Parkinson’s disease may be misdiagnosed due to a lack of clinical experience. Moreover, it is highly subjective and is not conducive to reflecting a true result. Due to the high incidence rate and increasing trend of PD, it is significant to use objective monitoring and diagnostic tools for accurate and timely diagnosis. In this paper, we designed a low-level feature extractor that uses convolutional layers to extract local information about an image and a high-level feature extractor that extracts global information about an image through the autofocus mechanism. PD is detected by fusing local and global information. The model is trained and evaluated on two publicly available datasets. Experiments have shown that our model has a strong advantage in diagnosing whether people have PD; gait-based analysis and recognition can also provide effective evidence for the early diagnosis of PD.