TITLE: A Hybrid VDV Model for Automatic Diagnosis of Pneumothorax using Class-Imbalanced Chest X-rays Dataset 

ABSTRACT: 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% Recall with 86.0% Area under the Receiver Operating
Characteristic curve (AUC) is attained. For the second dataset, 90.9% Recall
with 95.0% AUC is achieved with random split of data while 85.45% recall with
77.06% AUC is obtained with patient-wise split of data. For RS-NIH, the
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.
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% Recall with 86.0% Area under the Receiver Operating
Characteristic curve (AUC) is attained. For the second dataset, 90.9% Recall
with 95.0% AUC is achieved with random split of data while 85.45% recall with
77.06% AUC is obtained with patient-wise split of data. For RS-NIH, the
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.
A Hybrid VDV Model for Automatic Diagnosis of Pneumothorax using Class-Imbalanced Chest X-rays Dataset
http://arxiv.org/abs/2012.11911v1