Determination of DL-Based Bone Age Assessment

Accurate measurement of skeletal maturity or bone age is critical in the diagnosis of growth and endocrine abnormalities in children of developing age. Given the challenges of legacy approaches, DL-based solutions that are both trustworthy and time-efficient contribute to the proper identification of the relevant development process. The most important aspect of DL-based investigations is to guarantee that the essential medical images are free of noise and that the focal areas are more discernible and distinct. This necessitates meticulous picture processing. The image processing has been enriched in this study, and its success after being trained in several deep networks with the help of transfer learning has been demonstrated. As can be observed, the EfficientNetV2S model has the best result (6.32 MAE - mean absolute error).