COVID-DenseNet: A Deep Learning Architecture to Detect COVID-19 from Chest Radiology Images

Abstract

Coronavirus disease (COVID-19) is a pandemic infectious disease that has a severe risk of spreading rapidly. The quick identification and isolation of the affected persons is the very first step to fight against this virus. In this regard, chest radiology images have been proven to be an effective screening approach of COVID-19 affected patients. A number of AI based solutions have been developed to make the screening of radiological images faster and more accurate in detecting COVID-19. In this study, we are proposing a deep learning based approach using Densenet-121 to effectively detect COVID-19 patients. We incorporated transfer learning technique to leverage the information regarding radiology image learned by another model (CheXNet) which was trained on a huge Radiology dataset of 112,120 images. We trained and tested our model on COVIDx dataset containing 13,800 chest radiography images across 13,725 patients. To check the robustness of our model, we performed both two-class and three-class classifications and achieved 96.49% and 93.71% accuracy respectively. To further validate the consistency of our performance, we performed patient-wise k-fold cross-validation and achieved an average accuracy of 92.91% for three class task. Moreover, we performed an interpretability analysis using Grad-CAM to highlight the most important image regions in making a prediction. Besides ensuring trustworthiness, this explainability can also provide new insights about the critical factors regarding COVID-19. Finally, we developed a website that takes chest radiology images as input and generates probabilities of the presence of COVID-19 or pneumonia and a heatmap highlighting the probable infected regions. Code and models' weights are availabe.

Transfer Learning-Based Automatic Detection of Coronavirus Disease 2019 (COVID-19) from Chest X-ray Images

“…Owing to the limited available dataset related to COVID-19, the pre-trained neural networks can be utilized for diagnosis of COVID-19. However, these approaches applied on chest X-ray images are very limited till now [ 15 ]. To this end, the present study aimed to use an automated deep convolution neural network based pre-trained transfer models for detection and diagnosis of COVID-19 infection in chest X-rays.…”

A deep learning-based COVID-19 automatic diagnostic framework using chest X-ray images

“…COVID-19 and healthy [ 25 ]. Sarker et al proposed an approach using the Densenet-121 for effective detection COVID-19 patients and make use of another deep-learning model CheXNet which was already trained on radiological dataset [ 26 ]. Accuracies of 96.49% and 93.71% were obtained for binary and 3-class classifications, respectively.…”

“…The system should work fast to reduce workload and give results much faster than human experts. Unlike many existing works [ [25] , [26] , [27] , [28] , [29] , [30] ] that only consider a classification task on COVID-19 and non-COVID classes, the trained deep-learning network on comprehensive dataset belonging to various countries used in proposed work can extract the best region in the X-ray images to be further fed into the succeeding classifier network.…”

System for Quantitative Diagnosis of COVID-19-Associated Pneumonia Based on Superpixels With Deep Learning and Chest CT

“…With shorter training times, the shallower networks achieved results comparable to their deeper and more complex counterparts, enabling classification efficiency in medical imaging information close to state-of-the-art techniques, even when using minimal hardware. In [9][10][11] the authors tested various models based on DL and chest radiography images to diagnose patients infected with COVID-19.…”