Data Imbalance in PSNR Dataset for Bone Age Assessment: Challenges and Solutions

The PSNR dataset used in bone age assessment suffers from a data imbalance problem, where the number of images of certain age groups is much higher than the others. This means that the model is more likely to be biased towards the overrepresented age groups, leading to poorer performance on the underrepresented ones. For example, in the PSNR dataset, there are significantly more images of children between the ages of 2-10 years than there are of adolescents or young adults. This can result in the model being less accurate when predicting the ages of adolescents or young adults. To address this issue, researchers can use data augmentation to artificially increase the number of images of underrepresented age groups or use techniques such as oversampling or undersampling to balance the dataset. Another approach is to use transfer learning, where a pre-trained model is fine-tuned on the PSNR dataset to improve the accuracy of age predictions for all age groups.