INTRODUCTION:In the year 1895 the X-ray images were discovered. Since then the medical imaging has got advanced tremendously. Anyhow the methods of interpretation have started progressing only by the evolution of Computer aided Diagnosis(CAD). OBJECTIVES: To develop a Computer Aided Diagnosis (CAD) system to detect the bone fracture which helps the radiologists (or) the Orthopaedics by interpreting the medical images in short duration. METHODS: In this paper, an effective automated bone fracture detection is proposed using enhanced Haar Wavelet Transform, Scale-Invariant Feature Transform (SIFT) and back propagation neural network. The former two techniques are used for feature extraction and the latter one is used for classification of fracture images. Simultaneously, the usage of enhanced Haar Wavelet Transforms and SIFT are phenomenally improves the quality of the X-ray image. Further in this work, k-means clustering based 'Bag of Words' methods are used to extract enhanced features extracted from SIFT. The classification phase of this proposed technique uses the classical back propagation neural network that contains 1024 neurons in 3-layers. RESULTS: The experimental validation of this proposed scheme performed using nearly 300 different bone fractures x-ray images confirmed a better classification rate of 93.4%. CONCLUSIONS: The experimental results of the proposed computer aided technique are proven to be better than the detection technique facilitated with the traditional SIFT technique.

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For many data mining and machine learning applications predicting minority class samples from skewed unbalanced data sets is a crucial problem. To address this problem, we propose a majority filter-based minority prediction (MFMP) approach for unbalanced datasets. The MFMP adopts an unsupervised learning technique for selecting samples for supervised learning. The approach is based on two steps. In the first-step, minority samples are clustered and majority class samples that are out of minority classification regions are identified. This improves minority prediction rate. In the second step majority samples are randomly selected in individual clusters and this enhances majority prediction rate. Experimentally we studied the behavior of MFMP approach and compared with the traditional random under-sampling approach on a synthetic data set and three UCI repository datasets using the following classifiers: decision tree, k-nearest neighbor, Naive Bayes and Radial basis function network. Precision, Recall and F-Measure are used for evaluating performance of classifiers. The experimental evidence suggests that MFMP approach exhibits good prediction rates over minority and majority classes on all classifiers. Furthermore, the proposed approach outperforms the traditional random under-sampling approach. MFMP applied on the decision tree gave better prediction as compared to other classifiers studied.

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