Improvement of Big Data Stream Mining Technique for Automatic Bone Age Assessment

Abstract

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.