A Deep Transfer Learning Framework for Pneumonia Detection from Chest X-ray Images
Kh Tohidul Islam, Sudanthi Wijewickrema, Aaron M. Collins, Stephen O’Leary
Abstract
Pneumonia occurs when the lungs are infected by a bacterial, viral, or fungal infection. Globally, it is the largest solo infectious disease causing child mortality. Early diagnosis and treatment of this disease are critical to avoid death, especially in infants. Traditionally, pneumonia diagnosis was performed by expert radiologists and/or doctors by analysing X-ray images of the chest. Automated diagnostic methods have been developed in recent years as an alternative to expert diagnosis. Deep learning-based image processing has been shown to be effective in automated diagnosis of pneumonia. However, deep leaning typically requires a large number of labelled samples for training, which is time consuming and expensive to obtain in medical applications as it requires the input of human experts. Transfer learning, where a model pretrained for a task on an existing labelled database is adapted to be reused for a different but related task, is a common workaround to this issue. Here, we explore the use of deep transfer learning to diagnose pneumonia using X-ray images of the chest. We demonstrate that using two individual pretrained models as feature extractors and training an artificial neural network on these features is an effective way to diagnose pneumonia. We also show through experiments that the proposed method outperforms similar existing methods with respect to accuracy and time.