Yi Zheng
ABSTRACT
The significance of acupoint reflex zones in Traditional Chinese Medicine is well acknowledged, aiding considerably in the comprehension and learning of the theory. This study addresses the segmentation challenges brought forth by skin color variations and hand sizes in the identification of acupoint reflex zones. An enhanced semantic segmentation methodology for hand acupoint reflex zones employing an augmented Deeplabv3+ network is proposed. Initially, a hybrid attention module, integrating the merits of spatial attention and channel attention mechanisms, is utilized to refine the Deeplabv3+ network, thereby enhancing its feature selection proficiency for acupoint reflex zones. To further augment the ASPP module's capability in low-resolution feature extraction, a dilated convolution with a dilation rate of 4 is incorporated into the initial setup. Subsequently, the focal loss function is adopted to heighten the segmentation precision of intricate samples. Through experimental evaluations, a notable enhancement in the segmentation clarity of diminutive reflex zones is demonstrated, with a mIoU improvement of 3.09% compared to the original DeeplabV3+ network.
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