Intermediate Learning-Based Attention Regulated Densenet for Diagnosis of Covid-19

Abstract

Objectives: The AI-based Computer-Aided-Diagnosis of Chest X-Rays related to COVID-19 is very essential. Here we present an Attention regulated Pre-trained DenseNet-121with intermediate transfer learning as a Chest X-Ray image classifier to classify images according to three labels: COVID-19, pneumonia, and normal. Methods: We are proposing a new Attention regulated ImageNet pre-trained DenseNet-121 neural network architecture, which is retrained on NIH ChestX-ray14 data as an intermediate database before the actual COVID- 19 database. We also fine-tuned the last layer of this neural network with a suggested novelty called the output neuron-keeping technique. Before feeding the Covid-19 data we removed all other neurons corresponding to Chest X-ray14 pathology classes except the “No finding” and “Pneumonia” classes. A new third neuron with random weights and bias is created in the final layer to detect Covid-19 pathology. A DenseNet-121 is supported by a GRAD-CAM-based attention mechanism in achieving detection accuracy and localization of pathologies. The used Covid-19 dataset is a combination of 370 Pneumonia, 1255 Normal, and 439 COVID-19 Chest X-Ray images. We randomly took 50 pictures from each class for testing purposes, the remaining images are augmented more to improve DenseNet performance on a small COVID- 19 dataset. Findings: Our state-of-art model achieved 98.6% test accuracy since it misclassified one out of 50 Covid-19 images, and one out of 50 Pneumonia images, but all 50 normal Chest X-Ray images are classified with 100% accuracy. We compared our model with the other three state-of-theart models, particularly under three-class classification problems (Pneumonia,Covid-19, and Normal). The experimental result shows that the proposed model outperforms the existing three models by obtaining an accuracy of 98.6%. The GRAD-CAM generated heatmaps improved the interpretation of the infections. Novelty: The intermediate transfer learning, neuron keeping, and attention-regulated DenseNet are the highlight of the proposed work in achieving detection accuracy. The GRAD-CAM generated heatmaps indicate future research on COVID-19 diagnosis to analyze the severity of the infection. We believe this model may bridge the medical community and Artificial Intelligence gap. Keywords: Chest XRay14; Covid19 diagnosis; DenseNet121; GradCam; Neuron keeping