Explainable SE-MobileNet for Pneumonia detection integrated with robustness assessment using adversarial examples

Abstract

Pneumonia is a detrimental disease, especially for children, which is caused due to bacterial infection. X-ray images are frequently observed manually to find out the existence of pneumonia in a patient’s body. However, diagnosing pneumonia using X-ray images through manual observation by different health professionals may lead to different conclusions. Thus, an efficient autonomic system is required to diagnose pneumonia from X-ray images, and deep learning techniques, such as CNN-based approaches are frequently used to create such autonomy. To ease the process of pneumonia diagnosis, in this study, we have proposed the SE-MobileNet approach. We compared the performance of the proposed SE-MobileNet with the default version of MobileNetV2 integrated with transfer learning. Using the publicly available Kaggle dataset, it is observed that SE-MobileNet obtained 97.4% accuracy on a select test set against the 96.4% accuracy of MobileNetV2, and in 10-fold cross-validation, SE-MobileNet achieved an average of 95.92% accuracy against the 92.35% accuracy of MobileNetV2. Further comparison analysis proves that the SE-MobileNet model not only performs much better than the vanilla MobileNetV2 but also performs competitively against the literature. In addition, robustness evaluation has been introduced in this study where Fast Gradient Sign Method (FGSM) is performed to generate adversarial images. It is found that in robustness evaluation, SE-MobileNet also performs better compared to MobileNetV2. Finally, to validate the appropriateness of the learning of the model, Explainable AI (XAI) based techniques have been employed.