Multi Region-Based Feature Connected Layer (RB-FCL) of deep learning models for bone age assessment

Ari Wibisono, Petrus Mursanto

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Prediction of bone age from an x-ray is one of the methods in the medical field to support predicting endocrine gland disease, growth abnormalities, and genetic disorders. A decision support system to predict the bone age from the x-ray image has been implemented. It utilizes traditional machine learning methods and deep learning. We propose the Region-Based Feature Connected Layer (RB-FCL) from the essential segmented region of hand x-ray. We treat the deep learning models as the feature extraction for each region of the hand x-ray bone. The Feature Connected Layers are the output from the trained important region, such as 1-radius-ulna, 2-carpal, 3-metacarpal, 4-phalanges, and 5-ephypisis. DenseNet121, InceptionV3, and InceptionResNetV2 are the deep learning models that we used to train the critical region. From the evaluation results, the Mean Absolute Error (MAE) results produced is 6.97. This result is better compared to standard deep learning models, which are 9.41.

Original languageEnglish
Article number67
JournalJournal of Big Data
Volume7
Issue number1
DOIs
Publication statusPublished - 1 Dec 2020

Keywords

  • Bone age assessment
  • Deep learning
  • Region-Based Feature Connected Layer
  • X-ray images

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