Bone Age Estimation by Deep Learning in X-Ray Medical Images

Behnam Kiani Kalejahi, Saeed Meshgini, Sabalan Daneshvar, Ali Farzamnia

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)


Patient skeletal age estimation using a skeletal bone age assessment method is a time consuming and very boring process. Today, in order to overcome these deficiencies, computerized techniques are used to replace hand-held techniques in the medical industry, to the extent that this results in better evaluation. The purpose of this research is to minimize the problems of the division of existing systems with deep learning algorithms and the high accuracy of diagnosis. The evaluation of skeletal bone age is the most clinical application for the study of endocrinology, genetic disorders and growth in young people. This assessment is usually performed using the radiologic analysis of the left wrist using the GP (Greulich-Pyle) technique or the TW(Tanner-Whitehouse) technique. Both techniques have many disadvantages, including a lack of human deductions from observations as well as being time-consuming.

Original languageEnglish
Title of host publication2020 28th Iranian Conference on Electrical Engineering, ICEE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781728172965
ISBN (Print)9781728172972
Publication statusPublished - 26 Nov 2020
Externally publishedYes
Event28th Iranian Conference on Electrical Engineering - Tabriz, Iran, Islamic Republic of
Duration: 4 Aug 20206 Aug 2020
Conference number: 28

Publication series

NameIranian Conference on Electrical Engineering, ICEE
ISSN (Print)2164-7054
ISSN (Electronic)2642-9527


Conference28th Iranian Conference on Electrical Engineering
Abbreviated titleICEE 2020
Country/TerritoryIran, Islamic Republic of

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