The invention relates to the technical field of
scalp detection, in particular to a
scalp detection method and
system based on
deep learning, and the method comprises the following steps: extracting the
light intensity value distribution and spatial position value of image pixel points based on
scalp image data, analyzing the regional feature boundaries of an epidermal layer, a corium layer and a
hair follicle layer, and carrying out the partitioning
processing of the whole region, and generating a scalp layering segmentation result. According to the method, through combined analysis of a
light intensity value and a spatial position in scalp image data, layered region division is refined, and through quantification of
sebaceous gland distribution area, a relation between
hair follicle spacing and density and corium
layer thickness characteristics, interaction characteristics of
sebaceous gland secretion value,
hair follicle spacing and thickness value are dynamically analyzed; the fluctuation trend of the health state in the
time sequence is disclosed, the accuracy of health state analysis is improved, the efficiency and comprehensiveness of anomaly positioning are enhanced, and more scientific support is provided for
medical diagnosis and beauty care.