AN IMPROVED RANDOM FOREST APPROACH FOR PREDICTING TUBERCULOSIS
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 5, Pages 1739-1744
AbstractTuberculosis is one of the perilous infectious diseases that can be categorized by the evolution of tubercles in the tissues. This disease mainly affects the lungs and also the other parts of our body. Five stages are being used to detect tuberculosis disease. They are pre-processing an image, segmenting the lungs and Extracting the feature, Feature Selection and Classification. The optimal features are selected by Modified Random forest. Finally, Support Vector Machine classifier method is used for image classification. The proposed system accuracy results are better than the existing method inclassification.
Index Terms—Tuberculosis, Segmentation,K Means clustering, Feature extraction, GLCM approach, Modified Random Forest, SVM classifier.
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