A method and system for body part measurement for skin treatment

A method of training a plurality of neural networks used to control an electronic device to output a measurement of a dimension of a body part in an input image, the input image captured by an image capturing unit of the electronic device, the plurality of neural networks comprising an object detection deep neural network, OD-DNN and a segmentation deep neural network, S-DNN, the method comprising the steps:

• initializing neural network parameter values of the S-DNN and OD-DNN;
• obtaining a training data set comprising a plurality of images, each image including the body part and a reference object of a predefined dimension;
• annotating the reference object and the body part in the images of the training data set to generate an annotated training data set;
• dividing a part of the annotated training data set into a validation data set;
• inputting the annotated training data set to the OD-DNN such that the OD-DNN learns to detect the reference object in the input image and process the detected reference object to predict a pixel dimension of the reference object;
• inputting the annotated training data set to the S-DNN such that the S-DNN learns to predict a segmentation mask of the input image, wherein the segmentation mask differentiates image pixels of the body part from image pixels of a background region in the input image;
• validating, using the validation data set, a pixel dimension of the reference object learnt by the OD-DNN and a segmentation mask learnt by the S-DNN, and
• updating the neural network parameter values of the S-DNN and OD-DNN based on the validation.