TY - GEN
T1 - Improvement of big data stream mining technique for automatic bone age assessment
AU - Wibisono, Ari
AU - Adibah, Jihan
AU - Mursanto, Petrus
AU - Saputri, Mei Silviana
N1 - Funding Information:
We want to thank our faculty and Universitas Indonesia, for funding us through Hibah Publikasi Internasional Terindeks 9 (PIT 9) No: NKB-0011/UN2.R3.1/HKP.05.00/2019.
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/11/20
Y1 - 2019/11/20
N2 - Rapid technology growth has stimulated automated systems development in the medical field. Automatic bone age evaluation is an example of the implementation of this technology in the medical field. The automated assessment is done based on the left-hand X-ray images. This assessment helps the radiologist and pediatrician evaluate children's growth. A system that can generate a precise and reliable prediction is essential. Thus the main challenge is to determine the suitable technology that can generate a reliable forecast, mainly when working with a large quantity of data. Big data is a growing trend; practical computing challenges created by data streams can be found in several types of applications. It is known that data streams are usually obtained from sensors and monitors which accumulatively can make data very large in volume. This can result in the inability of real-time processing to be carried out. In this paper, the data stream technique is utilized to assess and predict bone ages. The analysis process is carried out in real-time when the data arrives so that the process of storing new data is done after the data is analyzed. A 9 GB sized-dataset consisting of 12,611 images were used. The images have various resolutions. We extracted and analyzed image features by using Canny Edge feature extraction. To predict bone age from those extracted features, we enhance the data stream mining technique base on tree stream mining. We use MAE or Mean Absolute Error, RMSE or Root Mean Squared Error, and MAPE or Mean Absolute Percentage Error as metrics in this measurement. The outcomes of our experiment show that our approach in data stream mining can increase performance measurements. The MAPE of our approach gives a 7% lower error evaluation compared to the standard method.
AB - Rapid technology growth has stimulated automated systems development in the medical field. Automatic bone age evaluation is an example of the implementation of this technology in the medical field. The automated assessment is done based on the left-hand X-ray images. This assessment helps the radiologist and pediatrician evaluate children's growth. A system that can generate a precise and reliable prediction is essential. Thus the main challenge is to determine the suitable technology that can generate a reliable forecast, mainly when working with a large quantity of data. Big data is a growing trend; practical computing challenges created by data streams can be found in several types of applications. It is known that data streams are usually obtained from sensors and monitors which accumulatively can make data very large in volume. This can result in the inability of real-time processing to be carried out. In this paper, the data stream technique is utilized to assess and predict bone ages. The analysis process is carried out in real-time when the data arrives so that the process of storing new data is done after the data is analyzed. A 9 GB sized-dataset consisting of 12,611 images were used. The images have various resolutions. We extracted and analyzed image features by using Canny Edge feature extraction. To predict bone age from those extracted features, we enhance the data stream mining technique base on tree stream mining. We use MAE or Mean Absolute Error, RMSE or Root Mean Squared Error, and MAPE or Mean Absolute Percentage Error as metrics in this measurement. The outcomes of our experiment show that our approach in data stream mining can increase performance measurements. The MAPE of our approach gives a 7% lower error evaluation compared to the standard method.
KW - Big data
KW - Bone age
KW - Data stream mining
KW - Decision support system
KW - FIMT-DD
UR - http://www.scopus.com/inward/record.url?scp=85079121950&partnerID=8YFLogxK
U2 - 10.1145/3372454.3372462
DO - 10.1145/3372454.3372462
M3 - Conference contribution
AN - SCOPUS:85079121950
T3 - ACM International Conference Proceeding Series
SP - 119
EP - 123
BT - ICBDR 2019 - Proceedings of the 2019 3rd International Conference on Big Data Research
PB - Association for Computing Machinery
T2 - 3rd International Conference on Big Data Research, ICBDR 2019
Y2 - 20 November 2019 through 21 November 2019
ER -