The investigation of multiresolution approaches for chest X-ray image based COVID-19 detection

Abstract

COVID-19 is a novel virus, which has a fast spreading rate, and now it is seen all around the world. The case and death numbers are increasing day by day. Some tests have been used to determine the COVID-19. Chest X-ray and chest computerized tomography (CT) are two important imaging tools for determination and monitoring of COVID-19. And new methods have been searching for determination of the COVID-19. In this paper, the investigation of various multiresolution approaches in detection of COVID-19 is carried out. Chest X-ray images are used as input to the proposed approach. As recent trend in machine learning shifts toward the deep learning, we would like to show that the traditional methods such as multiresolution approaches are still effective. To this end, the well-known multiresolution approaches namely Wavelet, Shearlet and Contourlet transforms are used to decompose the chest X-ray images and the entropy and the normalized energy approaches are employed for feature extraction from the decomposed chest X-ray images. Entropy and energy features are generally accompanied with the multiresolution approaches in texture recognition applications. The extreme learning machines (ELM) classifier is considered in the classification stage of the proposed study. A dataset containing 361 different COVID-19 chest X-ray images and 200 normal (healthy) chest X-ray images are used in the experimental works. The performance evaluation is carried out by employing various metric namely accuracy, sensitivity, specificity and precision. As deep learning is mentioned, a comparison between proposed multiresolution approaches and deep learning approaches is also carried out. To this end, deep feature extraction and fine-tuning of pretrained convolutional neural networks (CNNs) are considered. For deep feature extraction, pretrained, ResNet50 model is employed. For classification of the deep features, the Support Vector Machines (SVM) classifier is used. The ResNet50 model is also used in the fine-tuning. The experimental works show that multiresolution approaches produced better performance than the deep learning approaches. Especially, Shearlet transform outperformed at all. 99.29% accuracy score is obtained by using Shearlet transform.

DeepCoroNet: A deep LSTM approach for automated detection of COVID-19 cases from chest X-ray images

“…Ismael et al. [23] also used multiresolution approaches namely, Shearlets, Wavelets, and Contourlet transforms for COVID-19 detection. The researchers represented more improved results than the deep learning method’s achievements.…”

Deep learning applied to automatic disease detection using chest X‐rays

“…Adding processes that improve feature selection also improved performance, including correlation‐based feature selection, 60 feature categorisation with decision trees, 61,62 SVMs 63 and even handpicking features 64 …”

“…Some papers demonstrated that DNN outperformed traditional models 79 and reduced computational cost of detection with high performance while maintaining good performance, especially those with minimal convolutional and fully connected layers. Ismael 63 demonstrated that non‐DL methods can still be effective at detecting COVID‐19 pneumonia using multiresolution approaches.…”

COVID-19 infection map generation and detection from chest X-ray images

“…In order to automate COVID-19 detection/recognition from CXR images, several studies [21][22][23] have extracted features from the CXRs to utilize Support Vector Machines classifier. On the other hand, many studies [17,[24][25][26][27][28][29][30][31][32] have proposed to use deep Convolutional Neural Networks (CNNs).…”