Pediatric Bone Age Assessment using Deep Learning Models

• URL: http://arxiv.org/abs/2207.10169v1
• Date: Wed, 20 Jul 2022 19:43:38 GMT
• Title: Pediatric Bone Age Assessment using Deep Learning Models
• Authors: Aravinda Raman, Sameena Pathan, Tanweer Ali
• Abstract summary: Bone age assessment (BAA) is a standard method for determining the age difference between skeletal and chronological age. In this study, pre-trained models like VGG-16, InceptionV3, XceptionNet, and MobileNet are used to assess the bone age of the input data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bone age assessment (BAA) is a standard method for determining the age difference between skeletal and chronological age. Manual processes are complicated and necessitate the expertise of experts. This is where deep learning comes into play. In this study, pre-trained models like VGG-16, InceptionV3, XceptionNet, and MobileNet are used to assess the bone age of the input data, and their mean average errors are compared and evaluated to see which model predicts the best.

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