Open AccessA Novel Approach for Bone Age Assessment using Deep Learning
Author(s) -
Nishan B. Poojary,
Prathamesh G. Pokhare,
Pratik P. Poojary,
Charmi D. Raghavani,
Jayashree Khanapuri
Publication year - 2021
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit21731
Subject(s) - transfer of learning , convolutional neural network , bone age , deep learning , artificial intelligence , computer science , feature (linguistics) , machine learning , mean absolute error , artificial neural network , feature extraction , medicine , mean squared error , statistics , mathematics , linguistics , philosophy
In this paper, we propose a detailed approach to create a Bone age assessment model. Bone age assessment is a common medical practice in the assessment of child development, who are less than 18 years of age. In this proposed model, the Xception architecture is being used for transfer learning. Using feature extraction and transfer learning, the pre-trained convolutional neural network were custom trained. The dataset used for training the model is obtained from the Kaggle RNSA Bone Age dataset containing 12811 male and female bone images of different age groups. Finally, we were able to attain a mean absolute error (MAE) of 8.175 months in male and female patients, which aligns with our initial goal of achieving MAE in under a year.