DeepCOVIDExplainer: Explainable COVID-19 Diagnosis Based on Chest X-ray Images (2004.04582v3)

Abstract: Amid the coronavirus disease(COVID-19) pandemic, humanity experiences a rapid increase in infection numbers across the world. Challenge hospitals are faced with, in the fight against the virus, is the effective screening of incoming patients. One methodology is the assessment of chest radiography(CXR) images, which usually requires expert radiologist's knowledge. In this paper, we propose an explainable deep neural networks(DNN)-based method for automatic detection of COVID-19 symptoms from CXR images, which we call DeepCOVIDExplainer. We used 15,959 CXR images of 15,854 patients, covering normal, pneumonia, and COVID-19 cases. CXR images are first comprehensively preprocessed, before being augmented and classified with a neural ensemble method, followed by highlighting class-discriminating regions using gradient-guided class activation maps(Grad-CAM++) and layer-wise relevance propagation(LRP). Further, we provide human-interpretable explanations of the predictions. Evaluation results based on hold-out data show that our approach can identify COVID-19 confidently with a positive predictive value(PPV) of 91.6%, 92.45%, and 96.12%; precision, recall, and F1 score of 94.6%, 94.3%, and 94.6%, respectively for normal, pneumonia, and COVID-19 cases, respectively, making it comparable or improved results over recent approaches. We hope that our findings will be a useful contribution to the fight against COVID-19 and, in more general, towards an increasing acceptance and adoption of AI-assisted applications in the clinical practice.

• The paper introduces DeepCOVIDExplainer, which integrates explainable deep ensemble learning for COVID-19 detection using chest X-ray images.
• The paper details a preprocessing pipeline that standardizes CXR images using techniques like histogram equalization and unsharp masking to enhance diagnosis.
• The paper reports robust metrics with a positive predictive value of 96.12% for COVID-19 detection, offering transparent insights through Grad-CAM++ and LRP.

An Analysis of DeepCOVIDExplainer: Explainable COVID-19 Diagnosis via Chest X-ray Images

The academic paper titled "DeepCOVIDExplainer: Explainable COVID-19 Diagnosis Based on Chest X-ray Images" presents a detailed exploration of a deep learning-based method aimed at detecting COVID-19 symptoms from chest X-ray (CXR) images. The motivation behind this paper emerges from the need for effective screening mechanisms during the COVID-19 pandemic, particularly given the constraints of traditional methods, such as the reverse transcriptase-polymerase chain reaction (RT-PCR), which are resource-intensive and require significant time for results.

Central to the proposed method, named DeepCOVIDExplainer, is the integration of explainability in the deep neural networks (DNNs) utilized for diagnosis. The system relies on a neural ensemble method to classify CXR images into normal, pneumonia, and COVID-19 cases. The dataset employed encompasses 15,959 CXR images from 15,854 patients, ensuring substantial representation of each condition under investigation.

Summary of Methodology

The procedure begins with a meticulous preprocessing of CXR images, which involves steps such as contrast and edge enhancement, noise elimination, and inpainting of textual artifacts. Key steps include histogram equalization, Perona-Malik filter application, and unsharp masking to standardize and normalize image data for further analysis.

The diagnostic pipeline incorporates an ensemble learning approach, drawing on models such as DenseNet, ResNet, and VGGNet architectures, all of which were refined through transfer learning techniques. The ensemble framework features Softmax class posterior averaging and prediction maximization methods to synthesize the predictions of these differing models.

To enhance the transparency of diagnosis, the paper employs state-of-the-art methods like gradient-guided class activation maps (Grad-CAM++) and layer-wise relevance propagation (LRP). These methods illuminate class-discriminating regions on the CXR images, offering human-interpretable insights into the model's predictions.

Significant Results

The paper reports robust quantitative outcomes, evaluating performance via metrics such as precision, recall, and F1 score across the normal, pneumonia, and COVID-19 classes. DeepCOVIDExplainer achieves a positive predictive value of 96.12% for COVID-19 detection, along with a precision of 94.6%, recall of 94.3%, and an F1 score of 94.6% across the conditions examined. These metrics suggest its viability as a supportive diagnostic tool when compared to traditional methods and other emerging AI-based solutions.

Implications and Future Directions

This research underlines the potential for AI-assisted diagnosis as a complement to expert radiologist evaluations, especially in resource-constrained settings. The enhanced explainability via Grad-CAM++ and LRP can mitigate the opaqueness typically associated with "black box" AI models, thereby fostering trust and adoption in clinical environments.

For future developments, the authors suggest expansion and diversification of the dataset, incorporation of multiple imaging modalities (such as CT scans), and engagement with radiologists for external validation of model accuracy and interpretability. Moreover, potential integration with symbolic reasoning systems could provide more comprehensive decision-making support, embracing clinical data beyond imaging and fostering a synergistic AI-human diagnostic methodology.

In conclusion, while the DeepCOVIDExplainer is not intended to replace human radiologists, its development marks a step toward more efficient screening processes in clinical practice, with a specific focus on conditions like COVID-19 that strain healthcare systems periodically. As AI technologies continue to evolve, such interdisciplinary research will be instrumental in realizing practical, scalable, and transparent diagnostic solutions.