An Improved System For Emphysema Recognition Using Cnn Features Extraction And Adaboost-decision Treeclassifier

Résumé: In this work, a hybrid model composed of a CNN and a classical machine learning methodwas proposed to improve the classification of emphysema diseases. Firstly, we have proposeda pre-treatment step based on contrast adjustment in order to improve the performancesof the proposed model. Second, we extract the features from the deeper layers of the CNNclassifier, then we classify these features with decision tree and AdaBoost algorithm. Theproposed model is validated by usinga set of 168 manually annotated ROIs for each CTimage, comprising the three classes: normal tissue, centrilobular emphysema, and para-septal emphysema. The obtained results show that the hybrid model proposed in thiswork provides the best accuracy in the case of the AdaBoost-Decision Tree classifier.Acomparison with CNN, CNN-SVM and CNN-AdaBoost-Decision Tree classifier has beenperformed. As conclusion, the CNN-AdaBoost-Decision Tree classifier provide the bestresults with an accuracy of 100%

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