Support vector machines classification based on particle swarm optimization for bone age determination

• Published in: Applied soft computing Vol. 24; pp. 597 - 602
• Main Authors: Güraksın, Gür Emre, Haklı, Hüseyin, Uğuz, Harun
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2014
• Subjects: Bone age; Computer aided diagnosis; Particle swarm optimization; Support vector machines; Support vector machines; Computer aided diagnosis; Particle swarm optimization; Bone age
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2014.08.007

•This paper proposes a new approach for training support vector machines with a bone age determination system.•The proposed approach is a combination of particle swarm optimization (PSO) and support vector machines (SVMs).•The performance and accuracy of the proposed PSO–SVM algorithm are examined on a bone age data set.•The results obtained by PSO–SVM show that PSO–SVM is more effective than the previous study based on conventional SVM. The evaluation of bone development is a complex and time-consuming task for the physicians since it may cause intraobserver and interobserver differences. In this study, we present a new training algorithm for support vector machines in order to determine the bone age in young children from newborn to 6 years old. By the new algorithm, we aimed to assist the radiologists so as to eliminate the disadvantages of the methods used in bone age determination. To achieve this purpose, primarily feature extraction procedure was performed to the left hand wrist X-ray images by using image processing techniques and the features related with the carpal bones and distal epiphysis of radius bone were obtained. Then these features were used for the input arguments of the classifier. In the classification process, a new training algorithm for support vector machines was proposed by using particle swarm optimization. When training support vector machines, particle swarm optimization was used for generating a new training instance which will represent the whole training set of the related class by using the training set. Finally, these new instances were used as the support vectors and classification process was carried out by using these new instances. The performance of the proposed method was compared with the naive Bayes, k-nearest neighborhood, support vector machines and C4.5 algorithms. As a result, it was determined that the proposed method was found successful than the other methods for bone age determination with a classification performance of 74.87%.