Efficient adaptive enhanced adaboost based detection of spinal abnormalities by Machine learning approaches

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Spinal abnormalities are commonly occurring disorders that are caused by injuries, osteoporosis (benign) and neoplastic infiltration (malignant). Patients with one of the listed malignancies must undergo therapy as soon as possible to prevent the progress of the disease and to avoid further bone damage to preserve a better quality of life. Classification of patients into low or high-risk groups is an important step in diagnosing a disorder, which led biomedical and bioinformatics research teams to investigate the usage of machine learning (ML) technologies. In this paper, ML techniques are adapted to a public lumbar spine dataset to detect vertebral fractures. First, the dataset was preprocessed by using the contour-based hybrid median filter with histogram equalization. Then the Mask LSTM-based R.O.I. segmentation techniques are applied to segment the spinal images. Finally, the suggested stacked Adaptive Enhanced AdaBoost (AE-AdB) gets trained on whole images to enhance the accuracy of the data classification. Results of the experiment show that the attained accuracy (97.86%) of the AE-AdB classifier was significantly higher than that of the other classifiers, namely Convolution Neural Network (CNN) (74%) and Extreme Gradient Boosting Algorithm (XGBoost) (84%).