Explainable and Lightweight Model for COVID-19 Detection Using Chest Radiology Images
This addresses the need for generalizable and interpretable tools for medical professionals in COVID-19 diagnosis, though it appears incremental as it builds on existing methods.
The researchers tackled the problem of COVID-19 detection from chest radiology images by developing a CNN model with explainability via Grad-CAM, achieving performance comparable to state-of-the-art deep learning models.
Deep learning (DL) analysis of Chest X-ray (CXR) and Computed tomography (CT) images has garnered a lot of attention in recent times due to the COVID-19 pandemic. Convolutional Neural Networks (CNNs) are well suited for the image analysis tasks when trained on humongous amounts of data. Applications developed for medical image analysis require high sensitivity and precision compared to any other fields. Most of the tools proposed for detection of COVID-19 claims to have high sensitivity and recalls but have failed to generalize and perform when tested on unseen datasets. This encouraged us to develop a CNN model, analyze and understand the performance of it by visualizing the predictions of the model using class activation maps generated using (Gradient-weighted Class Activation Mapping) Grad-CAM technique. This study provides a detailed discussion of the success and failure of the proposed model at an image level. Performance of the model is compared with state-of-the-art DL models and shown to be comparable. The data and code used are available at https://github.com/aleesuss/c19.