An Ensemble Approach To Emphysema Classification

Any disease or disorder in the human body if recognized at an early stage, effective treatment can be given. Emphysema is a type of CPOD (Chronic obstructive pulmonary disease) and it is due to malfunctioning of lungs. Breathing becomes difficult with this disease. It occurs due to the stretching and damaging of air sacs in the lungs. In this work, an ensemble approach is proposed using the features extracted from the last global average pooling layer of the pre trained convolutional neural networks which are trained on ‘Imagenet’ data set. These features are given to an Error Correcting Output Code classifier with the base classifier being Support Vector Machine. Three best pre trained networks are selected based on the average classification accuracy. An ensemble of the three classifiers is considered. Based on the majority voting, weighed average probability and highest probability strategy, the test images labels are identified. Hold out validation (80% training and 20% testing) is used to assess the performance of the proposed algorithm. A popular database of computed tomography emphysema images is chosen to validate our proposal. A peak classification accuracy of 100% and an average classification accuracy of 95.88% is obtained with a combination of Resnet18, Shufflenet, and Resnet 101 pre trained networks with the averaged probability as a choice in the prediction of labels of test images. Compared to the state of the art approaches for classifying emphysema, the proposed method is superior in terms of classification accuracy.