In this work, the use of different Machine Learning classifiers with several features sets is presented to categorize Dual X-Ray Absorptiometry images according to the texture of their bone structure in two classes: control and osteoporosis. Two different bone areas were used: Ward’s Triangle and Femoral Neck. The diagnosis of this disease employing methods that analyze the bone structure through images of different medical modalities is an area of great interest. Support Vector Machine and Random Forest showed better AUC values than the rest of the methods. The features that gave the best results were those related to first-order statistics, which is consistent with what was found in previous work. For the combination of features and classification methods, the best results were 0.955 for AUC and 0.916 for F-Score using the Femoral Neck area. The obtained results constitute a reasonable basis for continuing the contributions to the subject by applying other learning methods such as deep learning.
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