A Portable Executable Clinical Decision Support Tool for Pneumonia Classification using Average Probability on an Ensemble Model

Abstract

Pneumonia is a respiratory illness that is highly infectious and affecting one or both lungs. It can lead to fatalities if left undiagnosed and untreated in time. While a radiologist can diagnose pneumonia just by looking at a chest X-ray, there are certain factors that may increase the chances of a misdiagnosis such as fatigue, multi-presenting symptoms, or inadequate overall experience in assessment of patients with low-prevalence presentations. A publicly available chest X-ray dataset was used in this study to create a portable executable clinical decision support tool to help determine the existence of pneumonia on a given chest X-ray image. ResNet50, DenseNet201, Xception, MobileNetV2, and ResNet101 were retrained to confirm pneumonia in chest X-rays both on the original and augmented dataset. An ensemble network was also constructed to improve the results by combining the strengths of the included pre-trained models using average probability. In this study, a total of 37 experiments were performed. Since there are instances when no single model excels on all metrics, the model with the highest Matthews Correlation Coefficient (MCC) was selected as the best model. MCC outputs an optimal score only if the classifier was able to get a high percentage for both positive and negative samples. To increase further the performance of the ensemble model, a novel method was introduced that determines which pre-trained model will give the greatest increase in the ensemble model performance when removed from the ensemble. The result is the identification of the best ensemble model variant consisting of only ResNet50, DenseNet201, Xception, and ResNet101 with Accuracy=93%, Precision=91%, Recall=98%, F1-Score=94%, Specificity=83%, MCC=85%. A stand-alone no-install application was created for this purpose to enable those with limited access or no access to internet to run the tool on their Window-based computer. Our tool can be used as portable stand-alone clinical decision support tool by radiologists when evaluating possible pneumonia case. It can also be used as a teaching tool for general practitioners and medical students.