Abstract: Abstract： In traditional hypopigmented skin disease diagnosis， reliance on the subjective clinical experience of dermatologists makes it challenging to ensure timely and accurate diagnoses for every patient. Therefore， there is a pressing need for a rapid， experience-independent diagnostic approach. Convolutional neural network （CNN） exhibits robust feature recognition capabilities， offering a potential solution. Currently， CNN -based diagnostic methods mainly focus on deeper models such as ResNet50. While achieving high accuracy， these models suffer from drawbacks like large parameter sizes， slow inference， and limited usability on mobile devices. To address these issues， this study introduces a novel lightweight CNN model based on MobileNetV3-Small. Firstly， it eliminates the computationally complex Squeeze-and-Excitation （SE） modules found in MobileNetV3-Small， replacing them with more lightweight Efficient Channel Attention （ECA） attention mechanism. Secondly， it employs the convenient and stable Leaky-ReLU activation function. Lastly， it introduces dilated convolutions in the convolutional layers to expand the receptive field. Experimental results indicate that the proposed model significantly reduces parameter size， recognition time and FLOPs compared to existing diagnostic models. It meets the high usability demands of mobile applications while still outperforming in terms of accuracy and F1 score. Ultimately， based on the proposed model， a mobile application for clinical diagnosis of hypopigmented skin disease has been developed.

Key words: Key words： hypopigmented skin disease, convolutional neural network, attention mechanism, activation function, dilated convolution