Diagnosing Children's Developmental Disorders By A Transfer Learning Based Architecture Using Knowledge Distillation

4 62 2 procedure, only the hand portion of the patient's X-ray remains for further evaluation.The subsequent phase involves the delineation of the boundaries of the region of interest.This is necessary because, in certain age groups, some bones are not ossified.Consequently, reliance on bones as landmarks is precluded.In the second step, The extracted ROI from the preceding step is utilized to train the teacher model.The student model utilizes the teacher model's knowledge to learn how to predict patient age.Therefore, the present study puts forth transfer learning methodologies founded on the distillation of knowledge, with the aim of facilitating the transference of knowledge between teacher and student models.The proposed method is based on the data set of the Digital Hand Atlas (DHA) database.The evaluation criteria used in this work are Accuracy, recall, permission and mean absolute error (MAE).The proposed model achieves 96/47% test accuracy for bone age classification.