Automatic classification of medical X-ray images using a bag of visual words

A novel approach is presented to gain high classification rate for each class of ImageCLEF 2007 medical database. The learning phase consists of four iterations where different classification models were generated as per iteration. For the iterations, a model generation process was performed in two steps. The first step starts with construction of a model from the entire dataset. This model was then assessed to filter high accuracy classes (HAC). These classes were those predicted with an accuracy rate above 80%. This evaluation performed on 20% of the training dataset was taken as test data. In the second step, classes under HAC were only used to construct the classification model. The same processes will be performed in the next iteration on the classes which were left with accuracy below 80% from the previous iteration. The methodology presented is based on a bag of visual words for feature extraction and the radial basis function (RBF)-based support vector machine classifier. As a result, four classification models were generated from 77, 17, 12 and 10 classes, respectively. These models were constructed and evaluated on a database consisting of 11 000 medical X-ray images (training dataset) and 1000 (testing dataset) of 116 classes. The accuracy rate obtained by each generated model outperformed the results obtained by only one model on the entire dataset.