COVIDLite: A depth-wise separable deep neural network with white balance and CLAHE for detection of COVID-19

Abstract

Background and Objective: Currently, the whole world is facing a pandemic disease, novel Coronavirus also known as COVID-19, which spread in more than 200 countries with around 3.3 million active cases and 4.4 lakh deaths approximately. Due to rapid increase in number of cases and limited supply of testing kits, availability of alternative diagnostic method is necessary for containing the spread of COVID-19 cases at an early stage and reducing the death count. For making available an alternative diagnostic method, we proposed a deep neural network based diagnostic method which can be easily integrated with mobile devices for detection of COVID-19 and viral pneumonia using Chest X-rays (CXR) images.Methods: In this study, we have proposed a method named COVIDLite, which is a combination of white balance followed by Contrast Limited Adaptive Histogram Equalization (CLAHE) and depth-wise separable convolutional neural network (DSCNN). In this method, white balance followed by CLAHE is used as an image preprocessing step for enhancing the visibility of CXR images and DSCNN trained using sparse cross entropy is used for image classification with lesser parameters and significantly lighter in size, i.e., 8.4 MB without quantization.Results: The proposed COVIDLite method resulted in improved performance in comparison to vanilla DSCNN with no pre-processing. The proposed method achieved higher accuracy of 99.58% for binary classification, whereas 96.43% for multi-class classification and out-performed various state-of-the-art methods.Conclusion: Our proposed method, COVIDLite achieved exceptional re-

Detecting COVID-19 infection status from chest X-ray and CT scan via single transfer learning-driven approach

“…We gathered the datasets from ARIA (2021) and Siddhartha and Santra (2020). Then we performed some pre-processing such that all of the photos are 224 × 224 pixels on both dataset.…”

“…In Figure 5B we showed the confusion matrix generated on X-ray images data Siddhartha and Santra (2020) to determine the performances of proposed model.…”

“…We proposed a DL based model aimed in identifying COVID-19 patients utilizing X-rays and CT scan data gathered from Siddhartha and Santra (2020) and ARIA (2021). The tests are carried out on a local machine with an i5-8265U processor, 8GB RAM, and Google-Collab for GPU.…”

RCoNet: Deformable Mutual Information Maximization and High-order Uncertainty-aware Learning for Robust COVID-19 Detection

“…Accordingly, deep learning based methods for detecting COVID-19 with chest X-ray (CXR) have been developed and shown to be able to achieve accurate and speedy detection [12], [13]. For instance, a tailored convolution neural network platform trained on open source dataset called COVIDNet in [14] was proposed for the detection of COVID-19 cases from CXR.…”

Chest X-ray Image Phase Features for Improved Diagnosis of COVID-19 Using Convolutional Neural Network

“…With data augmentation they have improved the overall accuracy 97.2%. In [10], Contrast Limited Adaptive Histogram Equalization (CLAHE) was used to enhance the CXR data. The authors proposed a depth-wise separable convolutional neural network (DSCNN) architecture.…”