4 research outputs found
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images
The outbreak of COVID-19 has shocked the entire world with its fairly rapid
spread and has challenged different sectors. One of the most effective ways to
limit its spread is the early and accurate diagnosis of infected patients.
Medical imaging such as X-ray and Computed Tomography (CT) combined with the
potential of Artificial Intelligence (AI) plays an essential role in supporting
the medical staff in the diagnosis process. Thereby, the use of five different
deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2, and
DenseNet161) and their Ensemble have been used in this paper, to classify
COVID-19, pneumoni{\ae} and healthy subjects using Chest X-Ray. Multi-label
classification was performed to predict multiple pathologies for each patient,
if present. Foremost, the interpretability of each of the networks was
thoroughly studied using techniques like occlusion, saliency, input X gradient,
guided backpropagation, integrated gradients, and DeepLIFT. The mean Micro-F1
score of the models for COVID-19 classifications ranges from 0.66 to 0.875, and
is 0.89 for the Ensemble of the network models. The qualitative results
depicted the ResNets to be the most interpretable model