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Whereas the worst one was SMA algorithm. & Cmert, Z. 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. Therefore, in this paper, we propose a hybrid classification approach of COVID-19. Software available from tensorflow. Netw. Automated detection of covid-19 cases using deep neural networks with x-ray images. Artif. Although outbreaks of SARS and MERS had confirmed human to human transmission3, they had not the same spread speed and infection power of the new coronavirus (COVID-19). Hence, the FC memory is applied during updating the prey locating in the second step of the algorithm to enhance the exploitation stage. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 19 (2015). Etymology. I am passionate about leveraging the power of data to solve real-world problems. COVID-19 image classification using deep features and fractional-order marine predators algorithm, $$\begin{aligned} \chi ^2=\sum _{k=1}^{n} \frac{(O_k - E_k)^2}{E_k} \end{aligned}$$, $$\begin{aligned} ni_{j}=w_{j}C_{j}-w_{left(j)}C_{left(j)}-w_{right(j)}C_{right(j)} \end{aligned}$$, $$\begin{aligned} fi_{i}=\frac{\sum _{j:node \mathbf \ {j} \ splits \ on \ feature \ i}ni_{j}}{\sum _{{k}\in all \ nodes }ni_{k}} \end{aligned}$$, $$\begin{aligned} normfi_{i}=\frac{fi_{i}}{\sum _{{j}\in all \ nodes }fi_{j}} \end{aligned}$$, $$\begin{aligned} REfi_{i}=\frac{\sum _{j \in all trees} normfi_{ij}}{T} \end{aligned}$$, $$\begin{aligned} D^{\delta }(U(t))=\lim \limits _{h \rightarrow 0} \frac{1}{h^\delta } \sum _{k=0}^{\infty }(-1)^{k} \begin{pmatrix} \delta \\ k\end{pmatrix} U(t-kh), \end{aligned}$$, $$\begin{aligned} \begin{pmatrix} \delta \\ k \end{pmatrix}= \frac{\Gamma (\delta +1)}{\Gamma (k+1)\Gamma (\delta -k+1)}= \frac{\delta (\delta -1)(\delta -2)\ldots (\delta -k+1)}{k! The dataset consists of 21,165 chest X-ray (CXR) COVID-19 images distributed on four categories which are COVID19, lung opacity, viral pneumonia, and NORMAL (Non-COVID). For example, Lambin et al.7 proposed an efficient approach called Radiomics to extract medical image features. Therefore, several pre-trained models have won many international image classification competitions such as VGGNet24, Resnet25, Nasnet26, Mobilenet27, Inception28 and Xception29. 2. Table3 shows the numerical results of the feature selection phase for both datasets. In Table4, for Dataset 1, the proposed FO-MPA approach achieved the highest accuracy in the best and mean measures, as it reached 98.7%, and 97.2% of correctly classified samples, respectively. The Marine Predators Algorithm (MPA)is a recently developed meta-heuristic algorithm that emulates the relation among the prey and predator in nature37. However, the proposed IMF approach achieved the best results among the compared algorithms in least time. This study aims to improve the COVID-19 X-ray image classification using feature selection technique by the regression mutual information deep convolution neuron networks (RMI Deep-CNNs). The model was developed using Keras library47 with Tensorflow backend48. Sahlol, A.T., Yousri, D., Ewees, A.A. et al. E. B., Traina-Jr, C. & Traina, A. J. 40, 2339 (2020). The accuracy measure is used in the classification phase. & Carlsson, S. Cnn features off-the-shelf: an astounding baseline for recognition. 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. HGSO was ranked second with 146 and 87 selected features from Dataset 1 and Dataset 2, respectively. 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. The data was collected mainly from retrospective cohorts of pediatric patients from Guangzhou Women and Childrens medical center. Layers are applied to extract different types of features such as edges, texture, colors, and high-lighted patterns from the images. Chowdhury, M.E. etal. Eng. Moreover, from Table4, it can be seen that the proposed FO-MPA provides better results in terms of F-Score, as it has the highest value in datatset1 and datatset2 which are 0.9821 and 0.99079, respectively. Cauchemez, S. et al. The proposed IMF approach successfully achieves two important targets, selecting small feature numbers with high accuracy. It based on using a deep convolutional neural network (Inception) for extracting features from COVID-19 images, then filtering the resulting features using Marine Predators Algorithm (MPA), enhanced by fractional-order calculus(FO). Appl. Syst. Covid-19 Classification Using Deep Learning in Chest X-Ray Images Abstract: Covid-19 virus, which has emerged in the Republic of China in an undetermined cause, has affected the whole world quickly. 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. Robustness-driven feature selection in classification of fibrotic interstitial lung disease patterns in computed tomography using 3d texture features. MathSciNet ADS Marine memory: This is the main feature of the marine predators and it helps in catching the optimal solution very fast and avoid local solutions. All data used in this paper is available online in the repository, [https://github.com/ieee8023/covid-chestxray-dataset], [https://stanfordmlgroup.github.io/projects/chexnet], [https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia] and [https://www.sirm.org/en/category/articles/covid-19-database/]. In the meantime, to ensure continued support, we are displaying the site without styles 43, 635 (2020). The evaluation outcomes demonstrate that ABC enhanced precision, and also it reduced the size of the features. Li et al.34 proposed a self-adaptive bat algorithm (BA) to address two problems in lung X-ray images, rebalancing, and feature selection. The classification accuracy of MPA, WOA, SCA, and SGA are almost the same. How- individual class performance. Then, applying the FO-MPA to select the relevant features from the images. Stage 2 has been executed in the second third of the total number of iterations when \(\frac{1}{3}t_{max}< t< \frac{2}{3}t_{max}\). The second CNN architecture classifies the X-ray image into three classes, i.e., normal, pneumonia, and COVID-19. In this subsection, a comparison with relevant works is discussed. Two real datasets about COVID-19 patients are studied in this paper. Our method is able to classify pneumonia from COVID-19 and visualize an abnormal area at the same time. 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. Evaluate the proposed approach by performing extensive comparisons to several state-of-art feature selection algorithms, most recent CNN architectures and most recent relevant works and existing classification methods of COVID-19 images. This stage can be mathematically implemented as below: In Eq. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Generally, the most stable algorithms On dataset 1 are WOA, SCA, HGSO, FO-MPA, and SGA, respectively. Stage 1: After the initialization, the exploration phase is implemented to discover the search space. \(r_1\) and \(r_2\) are the random index of the prey. where \(fi_{i}\) represents the importance of feature I, while \(ni_{j}\) refers to the importance of node j. The given Kaggle dataset consists of chest CT scan images of patients suffering from the novel COVID-19, other pulmonary disorders, and those of healthy patients. Automatic COVID-19 lung images classification system based on convolution neural network. The lowest accuracy was obtained by HGSO in both measures. 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. Sci. where CF is the parameter that controls the step size of movement for the predator. Epub 2022 Mar 3. In this paper, a new ML-method proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. 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. The combination of SA and GA showed better performances than the original SA and GA. Narayanan et al.33 proposed a fuzzy particle swarm optimization (PSO) as an FS method to enhance the classification of CT images of emphysema. Image segmentation is a necessary image processing task that applied to discriminate region of interests (ROIs) from the area of outsides. kharrat and Mahmoud32proposed an FS method based on a hybrid of Simulated Annealing (SA) and GA to classify brain tumors using MRI. To address this challenge, this paper proposes a two-path semi- supervised deep learning model, ssResNet, based on Residual Neural Network (ResNet) for COVID-19 image classification, where two paths refer to a supervised path and an unsupervised path, respectively. It is calculated between each feature for all classes, as in Eq. PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer. Ozturk et al. The family of coronaviruses is considered serious pathogens for people because they infect respiratory, hepatic, gastrointestinal, and neurologic diseases. We adopt a special type of CNN called a pre-trained model where the network is previously trained on the ImageNet dataset, which contains millions of variety of images (animal, plants, transports, objects,..) on 1000 classe categories. The results of max measure (as in Eq. arXiv preprint arXiv:1409.1556 (2014). Hashim, F. A., Houssein, E. H., Mabrouk, M. S., Al-Atabany, W. & Mirjalili, S. Henry gas solubility optimization: a novel physics-based algorithm. For this motivation, we utilize the FC concept with the MPA algorithm to boost the second step of the standard version of the algorithm. To survey the hypothesis accuracy of the models. Deep cnns for microscopic image classification by exploiting transfer learning and feature concatenation. The MCA-based model is used to process decomposed images for further classification with efficient storage. Decaf: A deep convolutional activation feature for generic visual recognition. Recently, a combination between the fractional calculus tool and the meta-heuristics opens new doors in providing robust and reliable variants41. Acharya et al.11 applied different FS methods to classify Alzheimers disease using MRI images. Also, some image transformations were applied, such as rotation, horizontal flip, and scaling. A.T.S. An efficient feature generation approach based on deep learning and feature selection techniques for traffic classification. Google Research, https://research.googleblog.com/2017/11/automl-for-large-scaleimage.html, Blog (2017). arXiv preprint arXiv:1704.04861 (2017). Such methods might play a significant role as a computer-aided tool for image-based clinical diagnosis soon. & Cmert, Z. 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. Eq. Credit: NIAID-RML }\delta (1-\delta )(2-\delta )(3-\delta ) U_{i}(t-3) + P.R\bigotimes S_i. 25, 3340 (2015). In our experiment, we randomly split the data into 70%, 10%, and 20% for training, validation, and testing sets, respectively. 0.9875 and 0.9961 under binary and multi class classifications respectively. An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm. Med. 22, 573577 (2014). They were manually aggregated from various web based repositories into a machine learning (ML) friendly format with accompanying data loader code. 11, 243258 (2007). In 2018 IEEE International Symposium on Circuits and Systems (ISCAS), 15 (IEEE, 2018). However, some of the extracted features by CNN might not be sufficient, which may affect negatively the quality of the classification images. Cite this article. As seen in Fig. I. S. of Medical Radiology. The proposed CNN architecture for Task 2 consists of 14 weighted layers, in which there are three convolutional layers and one fully connected layer, as shown in Fig. For the special case of \(\delta = 1\), the definition of Eq. Nguyen, L.D., Lin, D., Lin, Z. Med. The Shearlet transform FS method showed better performances compared to several FS methods. In ancient India, according to Aelian, it was . A features extraction method using the Histogram of Oriented Gradients (HOG) algorithm and the Linear Support Vector Machine (SVM), K-Nearest Neighbor (KNN) Medium and Decision Tree (DT) Coarse Tree classification methods can be used in the diagnosis of Covid-19 disease. So, there might be sometimes some conflict issues regarding the features vector file types or issues related to storage capacity and file transferring. The proposed IMF approach is employed to select only relevant and eliminate unnecessary features. layers is to extract features from input images. Med. This dataset currently contains hundreds of frontal view X-rays and is the largest public resource for COVID-19 image and prognostic data, making it a necessary resource to develop and evaluate tools to aid in the treatment of CO VID-19. The results are the best achieved compared to other CNN architectures and all published works in the same datasets. Narayanan, S.J., Soundrapandiyan, R., Perumal, B. and JavaScript. They were also collected frontal and lateral view imagery and metadata such as the time since first symptoms, intensive care unit (ICU) status, survival status, intubation status, or hospital location. Future Gener. Book Performance analysis of neural networks for classification of medical images with wavelets as a feature extractor. They applied a fuzzy decision tree classifier, and they found that fuzzy PSO improved the classification accuracy. Number of extracted feature and classification accuracy by FO-MPA compared to other CNNs on dataset 1 (left) and on dataset 2 (right). Propose similarity regularization for improving C. Fractional Differential Equations: An Introduction to Fractional Derivatives, Fdifferential Equations, to Methods of their Solution and Some of Their Applications Vol. They showed that analyzing image features resulted in more information that improved medical imaging. On the second dataset, dataset 2 (Fig. In Dataset 2, FO-MPA also is reported as the highest classification accuracy with the best and mean measures followed by the BPSO. Stage 2: The prey/predator in this stage begin exploiting the best location that detects for their foods. Although the performance of the MPA and bGWO was slightly similar, the performance of SGA and WOA were the worst in both max and min measures. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from . 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. Tensorflow: Large-scale machine learning on heterogeneous systems, 2015. }\delta (1-\delta ) U_{i}(t-1)+ \frac{1}{3! Multimedia Tools Appl. 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 implemented the FO-MPA swarm optimization and prepared the related figures and manuscript text. For the image features that led to categorization, there were varied levels of agreement in the interobserver and intraobserver components that . In this subsection, the results of FO-MPA are compared against most popular and recent feature selection algorithms, such as Whale Optimization Algorithm (WOA)49, Henry Gas Solubility optimization (HGSO)50, Sine cosine Algorithm (SCA), Slime Mould Algorithm (SMA)51, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO)52, Harris Hawks Optimization (HHO)53, Genetic Algorithm (GA), and basic MPA. The evaluation confirmed that FPA based FS enhanced classification accuracy. They shared some parameters, such as the total number of iterations and the number of agents which were set to 20 and 15, respectively. & Cao, J. The first one, dataset 1 was collected by Joseph Paul Cohen and Paul Morrison and Lan Dao42, where some COVID-19 images were collected by an Italian Cardiothoracic radiologist. Evaluation outcomes showed that GA based FS methods outperformed traditional approaches, such as filter based FS and traditional wrapper methods. The proposed IFM approach is summarized as follows: Extracting deep features from Inception, where about 51 K features were extracted. Apostolopoulos, I. D. & Mpesiana, T. A. Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. In some cases (as exists in this work), the dataset is limited, so it is not sufficient for building & training a CNN. Article Google Scholar. However, it has some limitations that affect its quality. 51, 810820 (2011). It is important to detect positive cases early to prevent further spread of the outbreak. Feature selection using flower pollination optimization to diagnose lung cancer from ct images. arXiv preprint arXiv:2003.13145 (2020). The results show that, using only 6 epochs for training, the CNNs achieved very high performance on the classification task. Memory FC prospective concept (left) and weibull distribution (right). The combination of Conv. Faramarzi, A., Heidarinejad, M., Mirjalili, S. & Gandomi, A. H. Marine predators algorithm: a nature-inspired metaheuristic. Objective: Lung image classification-assisted diagnosis has a large application market. arXiv preprint arXiv:2004.07054 (2020). MPA simulates the main aim for most creatures that is searching for their foods, where a predator contiguously searches for food as well as the prey. One of the main disadvantages of our approach is that its built basically within two different environments. In Iberian Conference on Pattern Recognition and Image Analysis, 176183 (Springer, 2011). Vis. It noted that all produced feature vectors by CNNs used in this paper are at least bigger by more than 300 times compared to that produced by FO-MPA in terms of the size of the featureset.