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

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.

[1]  Hao Wu,et al.  CXNet-m1: Anomaly Detection on Chest X-Rays With Image-Based Deep Learning , 2019, IEEE Access.

[2]  Ümit Budak,et al.  Transfer learning based histopathologic image classification for breast cancer detection , 2018, Health Information Science and Systems.

[3]  Jian Sun,et al.  Identity Mappings in Deep Residual Networks , 2016, ECCV.

[4]  K. Doi,et al.  Optimal image feature set for detecting lung nodules on chest X-ray images , 2002 .

[5]  Jeonghwan Gwak,et al.  Multiple Feature Integration for Classification of Thoracic Disease in Chest Radiography , 2019, Applied Sciences.

[6]  N. Sri Madhava Raja,et al.  Deep-learning framework to detect lung abnormality - A study with chest X-Ray and lung CT scan images , 2020, Pattern Recognit. Lett..

[7]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[8]  U. Rajendra Acharya,et al.  Automated detection of COVID-19 cases using deep neural networks with X-ray images , 2020, Computers in Biology and Medicine.

[9]  Abdulkadir Sengür,et al.  Cascaded deep learning-based efficient approach for license plate detection and recognition , 2020, Expert Syst. Appl..

[10]  Irene Cheng,et al.  Novel coarse-to-fine dual scale technique for tuberculosis cavity detection in chest radiographs , 2013, EURASIP J. Image Video Process..

[11]  O. M. Rijal,et al.  A statistical interpretation of the chest radiograph for the detection of pulmonary tuberculosis , 2010, 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES).

[12]  Algimantas Juozapavičius,et al.  Computer-aided detection of interstitial lung diseases: A texture approach , 2017 .

[13]  Atul Kumar,et al.  Distinguishing normal and pulmonary edema chest x-ray using Gabor filter and SVM , 2014, 2014 IEEE International Symposium on Bioelectronics and Bioinformatics (IEEE ISBB 2014).

[14]  G. Easley,et al.  Sparse directional image representations using the discrete shearlet transform , 2008 .

[15]  Deniz Korkmaz,et al.  COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images , 2020, Medical Hypotheses.

[16]  Zumray Dokur,et al.  X-Ray Chest Image Classification by A Small-Sized Convolutional Neural Network , 2019, 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT).

[17]  Abdulkadir Sengur,et al.  GA-SELM: Greedy algorithms for sparse extreme learning machine , 2014 .

[18]  Michel Barlaud,et al.  Image coding using wavelet transform , 1992, IEEE Trans. Image Process..

[19]  A. Ng,et al.  Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists , 2018, PLoS medicine.

[20]  Abdulkadir Sengür,et al.  Wavelet packet neural networks for texture classification , 2007, Expert Syst. Appl..

[21]  Minh N. Do,et al.  Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation , 2022 .

[22]  Bram van Ginneken,et al.  A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database , 2006, Medical Image Anal..

[23]  Giacomo Capizzi,et al.  Small lung nodules detection based on local variance analysis and probabilistic neural network , 2018, Comput. Methods Programs Biomed..

[24]  Chang Liu,et al.  TX-CNN: Detecting tuberculosis in chest X-ray images using convolutional neural network , 2017, 2017 IEEE International Conference on Image Processing (ICIP).