11, 243258 (2007). 152, 113377 (2020). In this paper, different Conv. Google Scholar. The announcement confirmed that from May 8, following Japan's Golden Week holiday period, COVID-19 will be officially downgraded to Class 5, putting the virus on the same classification level as seasonal influenza. For each decision tree, node importance is calculated using Gini importance, Eq. As Inception examines all X-ray images over and over again in each epoch during the training, these rapid ups and downs are slowly minimized in the later part of the training. Besides, the binary classification between two classes of COVID-19 and normal chest X-ray is proposed. Table3 shows the numerical results of the feature selection phase for both datasets. (33)), showed that FO-MPA also achieved the best value of the fitness function compared to others. Google Research, https://research.googleblog.com/2017/11/automl-for-large-scaleimage.html, Blog (2017). It is also noted that both datasets contain a small number of positive COVID-19 images, and up to our knowledge, there is no other sufficient available published dataset for COVID-19. Donahue, J. et al. Radiology 295, 2223 (2020). Decaf: A deep convolutional activation feature for generic visual recognition. Table4 show classification accuracy of FO-MPA compared to other feature selection algorithms, where the best, mean, and STD for classification accuracy were calculated for each one, besides time consumption and the number of selected features (SF). On the second dataset, dataset 2 (Fig. Shi, H., Li, H., Zhang, D., Cheng, C. & Cao, X. In this paper, we proposed a novel COVID-19 X-ray classification approach, which combines a CNN as a sufficient tool to extract features from COVID-19 X-ray images. By filtering titles, abstracts, and content in the Google Scholar database, this literature review was able to find 19 related papers to answer two research questions, i.e. COVID-19 image classification using deep learning: Advances, challenges and opportunities COVID-19 image classification using deep learning: Advances, challenges and opportunities Comput Biol Med. Google Scholar. Inf. Syst. The evaluation confirmed that FPA based FS enhanced classification accuracy. The Marine Predators Algorithm (MPA)is a recently developed meta-heuristic algorithm that emulates the relation among the prey and predator in nature37. PubMedGoogle Scholar. & Cmert, Z. Recombinant: A process in which the genomes of two SARS-CoV-2 variants (that have infected a person at the same time) combine during the viral replication process to form a new variant that is different . }\delta (1-\delta ) U_{i}(t-1)+ \frac{1}{3! ADS J. Med. what medical images are commonly used for COVID-19 classification and what are the methods for COVID-19 classification. 43, 635 (2020). The main contributions of this study are elaborated as follows: Propose an efficient hybrid classification approach for COVID-19 using a combination of CNN and an improved swarm-based feature selection algorithm. Design incremental data augmentation strategy for COVID-19 CT data. Future Gener. Methods Med. Currently, we witness the severe spread of the pandemic of the new Corona virus, COVID-19, which causes dangerous symptoms to humans and animals, its complications may lead to death. 2020-09-21 . Epub 2022 Mar 3. According to the promising results of the proposed model, that combines CNN as a feature extractor and FO-MPA as a feature selector could be useful and might be successful in being applied in other image classification tasks. 2. Compared to59 which is one of the most recent published works on X-ray COVID-19, a combination between You Only Look Once (YOLO) which is basically a real time object detection system and DarkNet as a classifier was proposed. They applied a fuzzy decision tree classifier, and they found that fuzzy PSO improved the classification accuracy. One of the best methods of detecting. where r is the run numbers. Eng. Such methods might play a significant role as a computer-aided tool for image-based clinical diagnosis soon. In this paper, Inception is applied as a feature extractor, where the input image shape is (229, 229, 3). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. It also contributes to minimizing resource consumption which consequently, reduces the processing time. In general, MPA is a meta-heuristic technique that simulates the behavior of the prey and predator in nature37. It is calculated between each feature for all classes, as in Eq. The prey follows Weibull distribution during discovering the search space to detect potential locations of its food. The algorithm combines the assessment of image quality, digital image processing and deep learning for segmentation of the lung tissues and their classification. Google Scholar. For each of these three categories, there is a number of patients and for each of them, there is a number of CT scan images correspondingly. COVID-19 tests are currently hard to come by there are simply not enough of them and they cannot be manufactured fast enough, which is causing panic. Comput. To segment brain tissues from MRI images, Kong et al.17 proposed an FS method using two methods, called a discriminative clustering method and the information theoretic discriminative segmentation. These datasets contain hundreds of frontal view X-rays and considered the largest public resource for COVID-19 image data. https://doi.org/10.1038/s41598-020-71294-2, DOI: https://doi.org/10.1038/s41598-020-71294-2. Comput. Sci Rep 10, 15364 (2020). Also, all other works do not give further statistics about their models complexity and the number of featurset produced, unlike, our approach which extracts the most informative features (130 and 86 features for dataset 1 and dataset 2) that imply faster computation time and, accordingly, lower resource consumption. J. (4). COVID-19 image classification using deep features and fractional-order marine predators algorithm Authors. We are hiring! Article They are distributed among people, bats, mice, birds, livestock, and other animals1,2. Number of extracted feature and classification accuracy by FO-MPA compared to other CNNs on dataset 1 (left) and on dataset 2 (right). In this paper, we propose an improved hybrid classification approach for COVID-19 images by combining the strengths of CNNs (using a powerful architecture called Inception) to extract features and a swarm-based feature selection algorithm (Marine Predators Algorithm) to select the most relevant features. & Mahmoud, N. Feature selection based on hybrid optimization for magnetic resonance imaging brain tumor classification and segmentation. Lett. In this paper, we used TPUs for powerful computation, which is more appropriate for CNN. Springer Science and Business Media LLC Online. SMA is on the second place, While HGSO, SCA, and HHO came in the third to fifth place, respectively. Cancer 48, 441446 (2012). & Pouladian, M. Feature selection for contour-based tuberculosis detection from chest x-ray images. FC provides a clear interpretation of the memory and hereditary features of the process. For Dataset 2, FO-MPA showed acceptable (not the best) performance, as it achieved slightly similar results to the first and second ranked algorithm (i.e., MPA and SMA) on mean, best, max, and STD measures. For the exploration stage, the weibull distribution has been applied rather than Brownian to bost the performance of the predator in stage 2 and the prey velocity in stage 1 based on the following formula: Where k, and \(\zeta\) are the scale and shape parameters. By achieving 98.7%, 98.2% and 99.6%, 99% of classification accuracy and F-Score for dataset 1 and dataset 2, respectively, the proposed approach outperforms several CNNs and all recent works on COVID-19 images. (8) can be remodeled as below: where \(D^1[x(t)]\) represents the difference between the two followed events. Furthermore, the proposed GRAY+GRAY_HE+GRAY_CLAHE image representation was evaluated on two different datasets, SARS-CoV-2 CT-Scan and New_Data_CoV2, where it was found to be superior to RGB . Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs. In this experiment, the selected features by FO-MPA were classified using KNN. chest X-ray images into three classes of COVID-19, normal chest X-ray and other lung diseases. Netw. Taking into consideration the current spread of COVID-19, we believe that these techniques can be applied as a computer-aided tool for diagnosing this virus. ), such as \(5\times 5\), \(3 \times 3\), \(1 \times 1\). The second CNN architecture classifies the X-ray image into three classes, i.e., normal, pneumonia, and COVID-19. JMIR Formative Research - Classifying COVID-19 Patients From Chest X-ray Images Using Hybrid Machine Learning Techniques: Development and Evaluation Published on 28.2.2023 in Vol 7 (2023) Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42324, first published August 31, 2022 . }\delta (1-\delta )(2-\delta )(3-\delta ) U_{i}(t-3) + P.R\bigotimes S_i. \(\Gamma (t)\) indicates gamma function. Chowdhury, M.E. etal. Havaei, M. et al. In this paper, we apply a convolutional neural network (CNN) to extract features from COVID-19 X-Ray images. where \(R\in [0,1]\) is a random vector drawn from a uniform distribution and \(P=0.5\) is a constant number. Computer Department, Damietta University, Damietta, Egypt, Electrical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum, Egypt, State Key Laboratory for Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China, Department of Applied Informatics, Vytautas Magnus University, Kaunas, Lithuania, Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt, School of Computer Science and Robotics, Tomsk Polytechnic University, Tomsk, Russia, You can also search for this author in Kharrat, A. 41, 923 (2019). Li et al.36 proposed an FS method using a discrete artificial bee colony (ABC) to improve the classification of Parkinsons disease. Kong, Y., Deng, Y. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770778 (2016). The proposed IFM approach is summarized as follows: Extracting deep features from Inception, where about 51 K features were extracted. and M.A.A.A. Mirjalili, S. & Lewis, A. In this paper, after applying Chi-square, the feature vector is minimized for both datasets from 51200 to 2000. Figure5 illustrates the convergence curves for FO-MPA and other algorithms in both datasets. Sohail, A. S.M., Bhattacharya, P., Mudur, S.P. & Krishnamurthy, S. Classification of ultrasound medical images using distance based feature selection and fuzzy-svm. Multimedia Tools Appl. The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. In 2018 IEEE International Symposium on Circuits and Systems (ISCAS), 15 (IEEE, 2018). & Carlsson, S. Cnn features off-the-shelf: an astounding baseline for recognition. Accordingly, the FC is an efficient tool for enhancing the performance of the meta-heuristic algorithms by considering the memory perspective during updating the solutions. You have a passion for computer science and you are driven to make a difference in the research community? Support Syst. The name "pangolin" comes from the Malay word pengguling meaning "one who rolls up" from guling or giling "to roll"; it was used for the Sunda pangolin (Manis javanica). All authors discussed the results and wrote the manuscript together. If the random solution is less than 0.2, it converted to 0 while the random solution becomes 1 when the solutions are greater than 0.2. The proposed approach selected successfully 130 and 86 out of 51 K features extracted by inception from dataset 1 and dataset 2, while improving classification accuracy at the same time. & Cmert, Z. After feature extraction, we applied FO-MPA to select the most significant features. PubMed Central Blog, G. Automl for large scale image classification and object detection. In14, the authors proposed an FS method based on a convolutional neural network (CNN) to detect pneumonia from lung X-ray images. arXiv preprint arXiv:1711.05225 (2017). The main purpose of Conv. 43, 302 (2019). Softw. In the meantime, to ensure continued support, we are displaying the site without styles Harikumar, R. & Vinoth Kumar, B. The model was developed using Keras library47 with Tensorflow backend48. MathSciNet Deep learning plays an important role in COVID-19 images diagnosis. Then the best solutions are reached which determine the optimal/relevant features that should be used to address the desired output via several performance measures. Since its structure consists of some parallel paths, all the paths use padding of 1 pixel to preserve the same height & width for the inputs and the outputs. \delta U_{i}(t)+ \frac{1}{2! Generally, the most stable algorithms On dataset 1 are WOA, SCA, HGSO, FO-MPA, and SGA, respectively. Also, As seen in Fig. The results indicate that all CNN-based architectures outperform the ViT-based architecture in the binary classification of COVID-19 using CT images. E. B., Traina-Jr, C. & Traina, A. J. It is obvious that such a combination between deep features and a feature selection algorithm can be efficient in several image classification tasks. Convolutional neural networks were implemented in Python 3 under Google Colaboratory46, commonly referred to as Google Colab, which is a research project for prototyping machine learning models on powerful hardware options such as GPUs and TPUs. Key Definitions. Therefore, reducing the size of the feature from about 51 K as extracted by deep neural networks (Inception) to be 128.5 and 86 in dataset 1 and dataset 2, respectively, after applying FO-MPA algorithm while increasing the general performance can be considered as a good achievement as a machine learning goal. The COVID-19 pandemic has been having a severe and catastrophic effect on humankind and is being considered the most crucial health calamity of the century. In COVID19 triage, DB-YNet is a promising tool to assist physicians in the early identification of COVID19 infected patients for quick clinical interventions. \(\bigotimes\) indicates the process of element-wise multiplications. The accuracy measure is used in the classification phase. Keywords - Journal. In Medical Imaging 2020: Computer-Aided Diagnosis, vol. Finally, the predator follows the levy flight distribution to exploit its prey location. Initialization phase: this phase devotes for providing a random set of solutions for both the prey and predator via the following formulas: where the Lower and Upper are the lower and upper boundaries in the search space, \(rand_1\) is a random vector \(\in\) the interval of (0,1). Table2 shows some samples from two datasets. Nguyen, L.D., Lin, D., Lin, Z. and A.A.E. In9, to classify ultrasound medical images, the authors used distance-based FS methods and a Fuzzy Support Vector Machine (FSVM). Use of chest ct in combination with negative rt-pcr assay for the 2019 novel coronavirus but high clinical suspicion. Thereafter, the FO-MPA parameters are applied to update the solutions of the current population. (22) can be written as follows: By using the discrete form of GL definition of Eq. For instance,\(1\times 1\) conv. However, the proposed IMF approach achieved the best results among the compared algorithms in least time. In this work, the MPA is enhanced by fractional calculus memory feature, as a result, Fractional-order Marine Predators Algorithm (FO-MPA) is introduced. Eng. PubMed Purpose The study aimed at developing an AI . Authors A., Fan, H. & Abd ElAziz, M. Optimization method for forecasting confirmed cases of covid-19 in china. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Transmission scenarios for middle east respiratory syndrome coronavirus (mers-cov) and how to tell them apart. Comput. Howard, A.G. etal. As a result, the obtained outcomes outperformed previous works in terms of the models general performance measure. CAS The results showed that the proposed approach showed better performances in both classification accuracy and the number of extracted features that positively affect resource consumption and storage efficiency. For example, Lambin et al.7 proposed an efficient approach called Radiomics to extract medical image features. (1): where \(O_k\) and \(E_k\) refer to the actual and the expected feature value, respectively. Expert Syst. }, \end{aligned}$$, $$\begin{aligned} D^{\delta }[U(t)]=\frac{1}{T^\delta }\sum _{k=0}^{m} \frac{(-1)^k\Gamma (\delta +1)U(t-kT)}{\Gamma (k+1)\Gamma (\delta -k+1)} \end{aligned}$$, $$\begin{aligned} D^1[U(t)]=U(t+1)-U(t) \end{aligned}$$, $$\begin{aligned} U=Lower+rand_1\times (Upper - Lower ) \end{aligned}$$, $$\begin{aligned} Elite=\left[ \begin{array}{cccc} U_{11}^1&{}U_{12}^1&{}\ldots &{}U_{1d}^1\\ U_{21}^1&{}U_{22}^1&{}\ldots &{}U_{2d}^1\\ \ldots &{}\ldots &{}\ldots &{}\ldots \\ U_{n1}^1&{}U_{n2}^1&{}\ldots &{}U_{nd}^1\\ \end{array}\right] , \, U=\left[ \begin{array}{cccc} U_{11}&{}U_{12}&{}\ldots &{}U_{1d}\\ U_{21}&{}U_{22}&{}\ldots &{}U_{2d}\\ \ldots &{}\ldots &{}\ldots &{}\ldots \\ U_{n1}&{}U_{n2}&{}\ldots &{}U_{nd}\\ \end{array}\right] , \, \end{aligned}$$, $$\begin{aligned} S_i&= {} R_B \bigotimes (Elite_i-R_B\bigotimes U_i), i=1,2,\ldots ,n \end{aligned}$$, $$\begin{aligned} U_i&= {} U_i+P.R\bigotimes S_i \end{aligned}$$, \(\frac{1}{3}t_{max}< t< \frac{2}{3}t_{max}\), $$\begin{aligned} S_i&= {} R_L \bigotimes (Elite_i-R_L\bigotimes U_i), i=1,2,\ldots ,n/2 \end{aligned}$$, $$\begin{aligned} S_i&= {} R_B \bigotimes (R_B \bigotimes Elite_i- U_i), i=1,2,\ldots ,n/2 \end{aligned}$$, $$\begin{aligned} U_i&= {} Elite_i+P.CF\bigotimes S_i,\, CF= \left( 1-\frac{t}{t_{max}} \right) ^{\left(2\frac{t}{t_{max}}\right) } \end{aligned}$$, $$\begin{aligned} S_i&= {} R_L \bigotimes (R_L \bigotimes Elite_i- U_i), i=1,2,\ldots ,n \end{aligned}$$, $$\begin{aligned} U_i&= {} Elite_i+P.CF\bigotimes S_i,\, CF= \left( 1-\frac{t}{t_{max}}\right) ^{\left(2\frac{t}{t_{max}} \right) } \end{aligned}$$, $$\begin{aligned} U_i=\left\{ \begin{array}{ll} U_i+CF [U_{min}+R \bigotimes (U_{max}-U_{min})]\bigotimes W &{} r_5 < FAD \\ U_i+[FAD(1-r)+r](U_{r1}-U_{r2}) &{} r_5 > FAD\\ \end{array}\right.
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