Volume 20 No 22 (2022)
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A Review on Pre-Processing Methods Utilized for Bone Age Estimation Models
Athar UL Haq Bhatt, Jimmy Singla
Abstract
In the present time, bone age estimation models are gaining popularity to measure the biological age of children's bones by evaluating their bones. In order to accomplish this goal, the researchers read the RSNA dataset of bone images. Next, the researchers pre-process the dataset to identify the region of interest. Further, machine learning algorithms are utilised to predict bone age. In this paper, we have studied and analysed the pre-processing methods used in the bone age estimation models because pre-processing methods enhance the dataset quality and effectively determine the region of interest, which enhances the machine learning algorithm performance to determine the bone age. This research primarily explores three areas, namely filtering, enhancement, and segmentation, in the pre-processing methods. From the analysis, we found that the metaheuristic algorithm is utilised to enhance the pre-processing methods by determining their optimal parameters. Finally, open research challenges are defined to enhance the pre-processing methods.
Keywords
Bone Age, Enhancement, Filtering, Machine Learning, Metaheuristic, Segmentation.
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