A Hybrid VDV Model for Automatic Diagnosis of Pneumothorax using Class-Imbalanced Chest X-rays Dataset
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Pneumothorax, a life threatening disease, needs to be diagnosed immediately and efficiently. The prognosis in this case is not only time consuming but also prone to human errors. So an automatic way of accurate diagnosis using chest X-rays is the utmost requirement. To-date, most of the available medical images datasets have class-imbalance issue. The main theme of this study is to solve this problem along with proposing an automated way of detecting pneumothorax. We first compare the existing approaches to tackle the class-imbalance issue and find that data-level-ensemble (i.e. ensemble of subsets of dataset) outperforms other approaches. Thus, we propose a novel framework named as VDV model, which is a complex model-level-ensemble of data-level-ensembles and uses three convolutional neural networks (CNN) including VGG16, VGG-19 and DenseNet-121 as fixed feature extractors. In each data-level-ensemble features extracted from one of the pre-defined CNN are fed to support vector machine (SVM) classifier, and output from each data-level-ensemble is calculated using voting method. Once outputs from the three data-level-ensembles with three different CNN architectures are obtained, then, again, voting method is used to calculate the final prediction. Our proposed framework is tested on SIIM ACR Pneumothorax dataset and Random Sample of NIH Chest X-ray dataset (RS-NIH). For the first dataset, 85.17 Characteristic curve (AUC) is attained. For the second dataset, 90.9 with 95.0 77.06 obtained results are higher as compared to previous results from literature However, for first dataset, direct comparison cannot be made, since this dataset has not been used earlier for Pneumothorax classification.
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