and A.A.E. Toaar, M., Ergen, B. Expert Syst. Sci Rep 10, 15364 (2020). Chollet, F. Xception: Deep learning with depthwise separable convolutions. However, using medical imaging, chest CT, and chest X-ray scan can play a critical role in COVID-19 diagnosis. In57, ResNet-50 CNN has been applied after applying horizontal flipping, random rotation, random zooming, random lighting, and random wrapping on raw images. The test accuracy obtained for the model was 98%. PubMed (9) as follows. We have used RMSprop optimizer for weight updates, cross entropy loss function and selected learning rate as 0.0001. Knowl. Med. In Eq. The HGSO also was ranked last. Da Silva, S. F., Ribeiro, M. X., Neto, Jd. (1): where \(O_k\) and \(E_k\) refer to the actual and the expected feature value, respectively. They compared the BA to PSO, and the comparison outcomes showed that BA had better performance. Cite this article. Inceptions layer details and layer parameters of are given in Table1. Abbas, A., Abdelsamea, M.M. & Gaber, M.M. Classification of covid-19 in chest x-ray images using detrac deep convolutional neural network. I am passionate about leveraging the power of data to solve real-world problems. and pool layers, three fully connected layers, the last one performs classification. Feature selection using flower pollination optimization to diagnose lung cancer from ct images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition12511258 (2017). (20), \(FAD=0.2\), and W is a binary solution (0 or 1) that corresponded to random solutions. Appl. SMA is on the second place, While HGSO, SCA, and HHO came in the third to fifth place, respectively. Donahue, J. et al. IEEE Signal Process. & Cao, J. & Cmert, Z. Also, in58 a new CNN architecture called EfficientNet was proposed, where more blocks were added on top of the model after applying normalization of images pixels intensity to the range (0 to 1). To further analyze the proposed algorithm, we evaluate the selected features by FO-MPA by performing classification. 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. Objective: Lung image classification-assisted diagnosis has a large application market. All classication models ever, the virus mutates, and new variants emerge and dis- performed better in classifying the Non-COVID-19 images appear. Besides, the used statistical operations improve the performance of the FO-MPA algorithm because it supports the algorithm in selecting only the most important and relevant features. & Baby, C.J. Emphysema medical image classification using fuzzy decision tree with fuzzy particle swarm optimization clustering. Eq. The GL in the discrete-time form can be modeled as below: where T is the sampling period, and m is the length of the memory terms (memory window). Furthermore, using few hundreds of images to build then train Inception is considered challenging because deep neural networks need large images numbers to work efficiently and produce efficient features. Diagnosis of parkinsons disease with a hybrid feature selection algorithm based on a discrete artificial bee colony. 2. (22) can be written as follows: By taking into account the early mentioned relation in Eq. We do not present a usable clinical tool for COVID-19 diagnosis, but offer a new, efficient approach to optimize deep learning-based architectures for medical image classification purposes. 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). To survey the hypothesis accuracy of the models. The classification accuracy of MPA, WOA, SCA, and SGA are almost the same. While55 used different CNN structures. Vis. Both datasets shared some characteristics regarding the collecting sources. The code of the proposed approach is also available via the following link [https://drive.google.com/file/d/1-oK-eeEgdCMCnykH364IkAK3opmqa9Rvasx/view?usp=sharing]. The proposed IMF approach is employed to select only relevant and eliminate unnecessary features. The proposed cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images, which can reliably localize infections of various shapes and sizes, especially small infection regions, which are rarely considered in recent studies. EMRes-50 model . Imag. 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 Therefore, in this paper, we propose a hybrid classification approach of COVID-19. Provided by the Springer Nature SharedIt content-sharing initiative, Environmental Science and Pollution Research (2023), Archives of Computational Methods in Engineering (2023), Arabian Journal for Science and Engineering (2023). Some people say that the virus of COVID-19 is. implemented the FO-MPA swarm optimization and prepared the related figures and manuscript text. Appl. Generally, the most stable algorithms On dataset 1 are WOA, SCA, HGSO, FO-MPA, and SGA, respectively. arXiv preprint arXiv:2003.13815 (2020). 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 family of coronaviruses is considered serious pathogens for people because they infect respiratory, hepatic, gastrointestinal, and neurologic diseases. Our dataset consisting of 60 chest CT images of COVID-19 and non-COVID-19 patients was pre-processed and segmented using a hybrid watershed and fuzzy c-means algorithm. According to the formula10, the initial locations of the prey and predator can be defined as below: where the Elite matrix refers to the fittest predators. However, WOA showed the worst performances in these measures; which means that if it is run in the same conditions several times, the same results will be obtained. Pool layers are used mainly to reduce the inputs size, which accelerates the computation as well. Computed tomography (CT) and magnetic resonance imaging (MRI) represent valuable input to AI algorithms, scanning human body sections for the sake of diagnosis. Szegedy, C. et al. In this work, the MPA is enhanced by fractional calculus memory feature, as a result, Fractional-order Marine Predators Algorithm (FO-MPA) is introduced. Moreover, the Weibull distribution employed to modify the exploration function. 4b, FO-MPA algorithm selected successfully fewer features than other algorithms, as it selected 130 and 86 features from Dataset 1 and Dataset 2, respectively. Classification Covid-19 X-Ray Images | by Falah Gatea | Medium 500 Apologies, but something went wrong on our end. (8) at \(T = 1\), the expression of Eq. Softw. The Softmax activation function is used for this purpose because the output should be binary (positive COVID-19 negative COVID-19). In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 19 (2015). I. S. of Medical Radiology. All authors discussed the results and wrote the manuscript together. Transmission scenarios for middle east respiratory syndrome coronavirus (mers-cov) and how to tell them apart. Aiming at the problems of poor attention to existing translation models, the insufficient ability of key transfer and generation, insufficient quality of generated images, and lack of detailed features, this paper conducts research on lung medical image translation and lung image classification based on . For instance,\(1\times 1\) conv. & Mahmoud, N. Feature selection based on hybrid optimization for magnetic resonance imaging brain tumor classification and segmentation. Inspired by our recent work38, where VGG-19 besides statistically enhanced Salp Swarm Algorithm was applied to select the best features for White Blood Cell Leukaemia classification. (8) can be remodeled as below: where \(D^1[x(t)]\) represents the difference between the two followed events. where \(ni_{j}\) is the importance of node j, while \(w_{j}\) refers to the weighted number of samples reaches the node j, also \(C_{j}\) determines the impurity value of node j. left(j) and right(j) are the child nodes from the left split and the right split on node j, respectively. \end{aligned} \end{aligned}$$, $$\begin{aligned} WF(x)=\exp ^{\left( {\frac{x}{k}}\right) ^\zeta } \end{aligned}$$, $$\begin{aligned}&Accuracy = \frac{\text {TP} + \text {TN}}{\text {TP} + \text {TN} + \text {FP} + \text {FN}} \end{aligned}$$, $$\begin{aligned}&Sensitivity = \frac{\text {TP}}{\text{ TP } + \text {FN}}\end{aligned}$$, $$\begin{aligned}&Specificity = \frac{\text {TN}}{\text {TN} + \text {FP}}\end{aligned}$$, $$\begin{aligned}&F_{Score} = 2\times \frac{\text {Specificity} \times \text {Sensitivity}}{\text {Specificity} + \text {Sensitivity}} \end{aligned}$$, $$\begin{aligned} Best_{acc} = \max _{1 \le i\le {r}} Accuracy \end{aligned}$$, $$\begin{aligned} Best_{Fit_i} = \min _{1 \le i\le r} Fit_i \end{aligned}$$, $$\begin{aligned} Max_{Fit_i} = \max _{1 \le i\le r} Fit_i \end{aligned}$$, $$\begin{aligned} \mu = \frac{1}{r} \sum _{i=1}^N Fit_i \end{aligned}$$, $$\begin{aligned} STD = \sqrt{\frac{1}{r-1}\sum _{i=1}^{r}{(Fit_i-\mu )^2}} \end{aligned}$$, https://doi.org/10.1038/s41598-020-71294-2. Johnson, D.S., Johnson, D. L.L., Elavarasan, P. & Karunanithi, A. While the second dataset, dataset 2 was collected by a team of researchers from Qatar University in Qatar and the University of Dhaka in Bangladesh along with collaborators from Pakistan and Malaysia medical doctors44. used a dark Covid-19 network for multiple classification experiments on Covid-19 with an accuracy of 87% [ 23 ]. Narayanan, S.J., Soundrapandiyan, R., Perumal, B. There are three main parameters for pooling, Filter size, Stride, and Max pool. As a result, the obtained outcomes outperformed previous works in terms of the models general performance measure. Kong, Y., Deng, Y. Havaei, M. et al. 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. used VGG16 to classify Covid-19 and achieved good results with an accuracy of 86% [ 22 ]. Syst. IEEE Trans. This dataset consists of 219 COVID-19 positive images and 1341 negative COVID-19 images. Fractional-order calculus (FC) gains the interest of many researchers in different fields not only in the modeling sectors but also in developing the optimization algorithms. Med. Comput. In addition, the good results achieved by the FO-MPA against other algorithms can be seen as an advantage of FO-MPA, where a balancing between exploration and exploitation stages and escaping from local optima were achieved. Scientific Reports Volume 10, Issue 1, Pages - Publisher. Robustness-driven feature selection in classification of fibrotic interstitial lung disease patterns in computed tomography using 3d texture features. They applied a fuzzy decision tree classifier, and they found that fuzzy PSO improved the classification accuracy. (4). where \(REfi_{i}\) represents the importance of feature i that were calculated from all trees, where \(normfi_{ij}\) is the normalized feature importance for feature i in tree j, also T is the total number of trees. In our experiment, we randomly split the data into 70%, 10%, and 20% for training, validation, and testing sets, respectively. Duan et al.13 applied the Gaussian mixture model (GMM) to extract features from pulmonary nodules from CT images. As seen in Table1, we keep the last concatenation layer which contains the extracted features, so we removed the top layers such as the Flatten, Drop out and the Dense layers which the later performs classification (named as FC layer). and M.A.A.A. We build the first Classification model using VGG16 Transfer leaning framework and second model using Deep Learning Technique Convolutional Neural Network CNN to classify and diagnose the disease and we able to achieve the best accuracy in both the model. Int. However, it was clear that VGG19 and MobileNet achieved the best performance over other CNNs. The updating operation repeated until reaching the stop condition. In order to normalize the values between 0 and 1 by dividing by the sum of all feature importance values, as in Eq. One of the best methods of detecting. Using X-ray images we can train a machine learning classifier to detect COVID-19 using Keras and TensorFlow. Such methods might play a significant role as a computer-aided tool for image-based clinical diagnosis soon. Isolation and characterization of a bat sars-like coronavirus that uses the ace2 receptor. FP (false positives) are the positive COVID-19 images that were incorrectly labeled as negative COVID-19, while FN (false negatives) are the negative COVID-19 images that were mislabeled as positive COVID-19 images. In this paper, we used two different datasets. For each decision tree, node importance is calculated using Gini importance, Eq. Syst. Coronavirus disease (Covid-19) is an infectious disease that attacks the respiratory area caused by the severe acute . Afzali et al.15 proposed an FS method based on principal component analysis and contour-based shape descriptors to detect Tuberculosis from lung X-Ray Images. & Pouladian, M. Feature selection for contour-based tuberculosis detection from chest x-ray images. Mutation: A mutation refers to a single change in a virus's genome (genetic code).Mutations happen frequently, but only sometimes change the characteristics of the virus. Nguyen, L.D., Lin, D., Lin, Z. Google Scholar. The following stage was to apply Delta variants. 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. Hence, it was discovered that the VGG-16 based DTL model classified COVID-19 better than the VGG-19 based DTL model. It achieves a Dice score of 0.9923 for segmentation, and classification accuracies of 0. The second one is based on Matlab, where the feature selection part (FO-MPA algorithm) was performed. Background The large volume and suboptimal image quality of portable chest X-rays (CXRs) as a result of the COVID-19 pandemic could post significant challenges for radiologists and frontline physicians. In this subsection, the performance of the proposed COVID-19 classification approach is compared to other CNN architectures. Among the FS methods, the metaheuristic techniques have been established their performance overall other FS methods when applied to classify medical images. To obtain Lilang Zheng, Jiaxuan Fang, Xiaorun Tang, Hanzhang Li, Jiaxin Fan, Tianyi Wang, Rui Zhou, Zhaoyan Yan: PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer. Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770778 (2016). In this work, we have used four transfer learning models, VGG16, InceptionV3, ResNet50, and DenseNet121 for the classification tasks. Syst. 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. In Inception, there are different sizes scales convolutions (conv. & Wang, W. Medical image segmentation using fruit fly optimization and density peaks clustering. Inspired by this concept, Faramarzi et al.37 developed the MPA algorithm by considering both of a predator a prey as solutions. Software available from tensorflow. Kharrat, A. Eurosurveillance 18, 20503 (2013). Article Image Underst. Extensive evaluation experiments had been carried out with a collection of two public X-ray images datasets. Objective: To help improve radiologists' efficacy of disease diagnosis in reading computed tomography (CT) images, this study aims to investigate the feasibility of applying a modified deep learning (DL) method as a new strategy to automatically segment disease-infected regions and predict disease severity. 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). They achieved 98.08 % and 96.51 % of accuracy and F-Score, respectively compared to our approach with 98.77 % and 98.2% for accuracy and F-Score, respectively. Afzali, A., Mofrad, F.B. 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. (2) calculated two child nodes. arXiv preprint arXiv:1409.1556 (2014). Evaluation outcomes showed that GA based FS methods outperformed traditional approaches, such as filter based FS and traditional wrapper methods. Figure7 shows the most recent published works as in54,55,56,57 and44 on both dataset 1 and dataset 2. My education and internships have equipped me with strong technical skills in Python, deep learning models, machine learning classification, text classification, and more. 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. They applied the SVM classifier with and without RDFS. However, the proposed FO-MPA approach has an advantage in performance compared to other works. 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. 0.9875 and 0.9961 under binary and multi class classifications respectively. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from . Table3 shows the numerical results of the feature selection phase for both datasets. Also, in12, an Fs method based on SVM was proposed to detect Alzheimers disease from SPECT images. Furthermore, deep learning using CNN is considered one of the best choices in medical imaging applications20, especially classification. Sci. chest X-ray images into three classes of COVID-19, normal chest X-ray and other lung diseases. Its structure is designed based on experts' knowledge and real medical process. In the last two decades, two famous types of coronaviruses SARS-CoV and MERS-CoV had been reported in 2003 and 2012, in China, and Saudi Arabia, respectively3. Also, it has killed more than 376,000 (up to 2 June 2020) [Coronavirus disease (COVID-2019) situation reports: (https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/)]. 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 .

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