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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.<\/jats:p>","DOI":"10.1186\/s40537-020-00347-0","type":"journal-article","created":{"date-parts":[[2020,8,28]],"date-time":"2020-08-28T13:03:27Z","timestamp":1598619807000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Multi Region-Based Feature Connected Layer (RB-FCL) of deep learning models for bone age assessment"],"prefix":"10.1186","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2652-3227","authenticated-orcid":false,"given":"Ari","family":"Wibisono","sequence":"first","affiliation":[]},{"given":"Petrus","family":"Mursanto","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,8,28]]},"reference":[{"issue":"3","key":"347_CR1","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1148\/129.3.661","volume":"129","author":"AK Poznanski","year":"1978","unstructured":"Poznanski AK, Hernandez RJ, Guire KE, Bereza UL, Garn SM. 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