The machine learning and artificial intelligence
play a vital role to solve the challenging issues in Clinical
imaging. The machine learning and artificial intelligence
ease the daily life of both medical practitioner and
patient’s. Nowadays, the automatic system is designed with
high accuracy to perceive abnormality in bone X-ray
images. To achieve high accuracy system has less resource
available image pre-processing tools are used to enhance
the medical images quality. The image pre-processing
involves the process like noise removal and contrast
enhancement which provides instantaneous abnormality
diagnosis system. The Gray Level Co-occurrence Matrix
(GLCM) texture features are widely used in image
classification problems. GLCM represents the secondorder statistical information of gray levels between
neighboring pixels in an image[1]. In the paper, we
implemented different machine learning approaches to
classify the bone X-ray images of MURA (musculoskeletal
radiographs) dataset into fractures and no fracture
category. The four different classifiers LBF SVM (Radial
Basis Function support vector machine), linear SVM,
Logistic Regression and Decision tree are used for
abnormality detection. The performance evaluation of the
above abnormality detection in X-ray images is performed
by using five statistical parameters such as Sensitivity,
Specificity, Precision, Accuracy and F1 Score, which shows
significant improvement.