A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images

Abstract

The world is suffering from an existential global health crisis known as the COVID-19 pandemic. Countries like India, Bangladesh, and other developing countries are still having a slow pace in the detection of COVID-19 cases. Therefore, there is an urgent need for fast detection with clear visualization of infection is required using which a suspected patient of COVID-19 could be saved. In the recent technological advancements, the fusion of deep learning classifiers and medical images provides more promising results corresponding to traditional RT-PCR testing while making detection and predictions about COVID-19 cases with increased accuracy. In this paper, we have proposed a deep transfer learning algorithm that accelerates the detection of COVID-19 cases by using X-ray and CT-Scan images of the chest. It is because, in COVID-19, initial screening of chest X-ray (CXR) may provide significant information in the detection of suspected COVID-19 cases. We have considered three datasets known as 1) COVID-chest X-ray, 2) SARS-COV-2 CT-scan, and 3) Chest X-Ray Images (Pneumonia). In the obtained results, the proposed deep learning model can detect the COVID-19 positive cases in ≤ 2 seconds which is faster than RT-PCR tests currently being used for detection of COVID-19 cases. We have also established a relationship between COVID-19 patients along with the Pneumonia patients which explores the pattern between Pneumonia and COVID-19 radiology images. In all the experiments, we have used the Grad-CAM based color visualization approach in order to clearly interpretate the detection of radiology images and taking further course of action.

A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence

“…Based on these attributes, the studies on X-ray and CT imaging modality for automated COVID-19 diagnosis are compared, respectively, in Tables 5 and 6 . For the 1 st attribute (number of subjects), some remarkable studies were found ([ 203 ] [ 204 ] [ 205 ] [ 206 ] [ 207 ] [ 208 ]) that used a cohort of over 1,000 subjects. The 2 nd attribute (number of classes) was broadly divided into binary and multiclass.…”

“…The 2 nd attribute (number of classes) was broadly divided into binary and multiclass. More than half of the selected studies had binary classification [ 209 ] [ 210 ] [ 211 ] [ 212 ] [ 203 ] [ 213 ] [ 204 ] [ 214 ] [ 215 ] [ 216 ], while the rest had multiclass. For the 3 rd attribute (2-D vs. 3-D), two studies ([ 210 ] and [ 212 ]) used a 3-D CT volume as input, while the rest used 2-D X-ray images.…”

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

“…Grad-CAM based colour-visualisation approach is also employed for a more visual interpretation of CXR images through deep learning models. It took around 2 s time to process a single CXR image [ 28 ].…”

“…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.…”

COVID-19 Detection by Optimizing Deep Residual Features with Improved Clustering-Based Golden Ratio Optimizer

“…However, Soares et al [20] implemented xDNN for classification and achieved an accuracy of 97.38% on the same dataset. Panwar et al [34] implemented Gradient-weighted Class Activation Mapping (Grad-CAM) on the same dataset and scored an accuracy of 95.61%. Again, Ozturk et al [21] implemented the DarkCovidNet model that produced an accuracy of 97.08% on the Chest X-ray dataset that was proposed by Muhammad Talo.…”