In Medical hypotheses
Humerus fracture have been widely seen disease in the orthopedic clinics and classification of them is a hard process for orthopedist. The main aim of the proposed method is to classify humerus fracture by using a naïve and multileveled method. We collected a novel humerus fracture X-ray image dataset. This dataset consists of 115 images. In this paper, a novel stable feature extraction method is presented to classify humerus fractures. This method is called exemplar pyramid method and it is inspired by exemplar facial expression recognition methods. To classify humerus fractures, X-ray images were employed as input. In this study, X-ray images are resized to 512 × 512 sized image. Then, the used humerus fracture images are divided into 64 × 64 size of exemplars. To create levels, maximum pooling which has been mostly used in deep networks is used and four levels are created. Histogram of oriented gradients (HOG) and local binary pattern (LBP) are employed for feature generation. The most discriminative ones of the generated and concatenated features are selected by using ReliefF and Neighborhood Component Analysis (NCA) based two levelled feature selector (RFNCA). To emphasize success of the proposed exemplar pyramid model based feature generation, four conventional classifiers are chosen for classification and the proposed exemplar pyramid model achieved 99.12% classification accuracy by using leave one out cross validation (LOOCV). Results and tests clearly illustrates success of the proposed exemplar pyramid model based humerus fracture classification method. The results also shown that the proposed exemplar pyramid model achieved higher classification rate than Orthopedist specialized in shoulder.
Demir Sukru, Key Sefa, Tuncer Turker, Dogan Sengul
Exemplar pyramid model, HOG, Humerus fracture classification, LBP, Machine learning, Orthopedic