Recognition of clock drawing test based on improved YOLOv7-tiny
The clock-drawing test is an important medical method for screening people with cognitive impairment.Aiming at the problems of different target scales and category imbalances in the current clock-drawing test,this paper proposes a clock-draw-ing test recognition algorithm based on improved YOLOv7-tiny.In order to improve the problem of false detection and missed detec-tion caused by scale changes and small-scale target detection,BiFPN bidirectional feature pyramid structure is introduced.The bi-directional information transmission mechanism can effectively integrate different levels of features and capture richer context and detailed information in different scale features.In order to improve the recognition accuracy of the category imbalance index,the WDLoss loss function is used to calculate the loss to improve the sensitivity of small target recognition.This paper creates a clock-drawing test data set based on cognitively impaired groups.Experiments on this data set show that the improved YOLOv7-tiny algo-rithm has a mAP of 94.28%for all categories of the clock drawing test data set,which is 1.13%higher than the original YOLOv7-tiny model,and the AP of the pointer in the unbalanced category is increased by 12.2%.
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