(5). More so, a combination of partial differential equations and deep learning was applied for medical image classification by10. Lambin, P. et al. Kong, Y., Deng, Y. Syst. In Inception, there are different sizes scales convolutions (conv. Slider with three articles shown per slide. COVID-19 image classification using deep features and fractional-order marine predators algorithm Authors. 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. 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. Moreover, a multi-objective genetic algorithm was applied to search for the optimal features subset. The conference was held virtually due to the COVID-19 pandemic. 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. Nevertheless, a common mistake in COVID-19 dataset fusion, mainly on classification tasks, is that by mixing many datasets of COVID-19 and using as Control images another dataset, there will be . In my thesis project, I developed an image classification model to detect COVID-19 on chest X-ray medical data using deep learning models such . 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. PubMedGoogle Scholar. contributed to preparing results and the final figures. In this subsection, a comparison with relevant works is discussed. CNNs are more appropriate for large datasets. 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. 2 (right). Eng. Inspired by this concept, Faramarzi et al.37 developed the MPA algorithm by considering both of a predator a prey as solutions. 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. This study presents an investigation on 16 pretrained CNNs for classification of COVID-19 using a large public database of CT scans collected from COVID-19 patients and non-COVID-19 subjects. MATH Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs. }\delta (1-\delta ) U_{i}(t-1)+ \frac{1}{3! They are distributed among people, bats, mice, birds, livestock, and other animals1,2. Chong et al.8 proposed an FS model, called Robustness-Driven FS (RDFS) to select futures from lung CT images to classify the patterns of fibrotic interstitial lung diseases. }, \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. & Wang, W. Medical image segmentation using fruit fly optimization and density peaks clustering. In this paper, different Conv. SharifRazavian, A., Azizpour, H., Sullivan, J. We can call this Task 2. Knowl. Regarding the consuming time as in Fig. M.A.E. The focus of this study is to evaluate and examine a set of deep learning transfer learning techniques applied to chest radiograph images for the classification of COVID-19, normal (healthy), and pneumonia. 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. The combination of Conv. Article According to the best measure, the FO-MPA performed similarly to the HHO algorithm, followed by SMA, HGSO, and SCA, respectively. 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. 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/)]. 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 . The two datasets consist of X-ray COVID-19 images by international Cardiothoracic radiologist, researchers and others published on Kaggle. Comput. 25, 3340 (2015). Authors The largest features were selected by SMA and SGA, respectively. The Shearlet transform FS method showed better performances compared to several FS methods. The . In ancient India, according to Aelian, it was . If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. As seen in Fig. The definitions of these measures are as follows: where TP (true positives) refers to the positive COVID-19 images that were correctly labeled by the classifier, while TN (true negatives) is the negative COVID-19 images that were correctly labeled by the classifier. In this paper, we used TPUs for powerful computation, which is more appropriate for CNN. They applied the SVM classifier with and without RDFS. Therefore, several pre-trained models have won many international image classification competitions such as VGGNet24, Resnet25, Nasnet26, Mobilenet27, Inception28 and Xception29. 97, 849872 (2019). Faramarzi et al.37 implement this feature via saving the previous best solutions of a prior iteration, and compared with the current ones; the solutions are modified based on the best one during the comparison stage. This algorithm is tested over a global optimization problem. For each decision tree, node importance is calculated using Gini importance, Eq. 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). The results indicate that all CNN-based architectures outperform the ViT-based architecture in the binary classification of COVID-19 using CT images. arXiv preprint arXiv:1409.1556 (2014). Automated detection of covid-19 cases using deep neural networks with x-ray images. 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. 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. medRxiv (2020). Comparison with other previous works using accuracy measure. One of these datasets has both clinical and image data. Efficient classification of white blood cell leukemia with improved swarm optimization of deep features. volume10, Articlenumber:15364 (2020) MathSciNet COVID-19 image classification using deep features and fractional-order marine predators algorithm. The proposed COVID-19 X-ray classification approach starts by applying a CNN (especially, a powerful architecture called Inception which pre-trained on Imagnet dataset) to extract the discriminant features from raw images (with no pre-processing or segmentation) from the dataset that contains positive and negative COVID-19 images. arXiv preprint arXiv:2003.13145 (2020). (4). Med. Generally, the proposed FO-MPA approach showed satisfying performance in both the feature selection ratio and the classification rate. They applied a fuzzy decision tree classifier, and they found that fuzzy PSO improved the classification accuracy. Inception architecture is described in Fig. Also, WOA algorithm showed good results in all measures, unlike dataset 1, which can conclude that no algorithm can solve all kinds of problems. (22) can be written as follows: By taking into account the early mentioned relation in Eq. Harris hawks optimization: algorithm and applications. One of the best methods of detecting. 43, 302 (2019). Imaging Syst. COVID-19 is the most transmissible disease, caused by the SARS-CoV-2 virus that severely infects the lungs and the upper respiratory tract of the human body.This virus badly affected the lives and wellness of millions of people worldwide and spread widely. In this work, the MPA is enhanced by fractional calculus memory feature, as a result, Fractional-order Marine Predators Algorithm (FO-MPA) is introduced. Intell. For both datasets, the Covid19 images were collected from patients with ages ranging from 40-84 from both genders. ADS With accounting the first four previous events (\(m=4\)) from the memory data with derivative order \(\delta\), the position of prey can be modified as follow; Second: Adjusting \(R_B\) random parameter based on weibull distribution. Comput. Zhang, N., Ruan, S., Lebonvallet, S., Liao, Q. Currently, a new coronavirus, called COVID-19, has spread to many countries, with over two million infected people or so-called confirmed cases. The MPA starts with the initialization phase and then passing by other three phases with respect to the rational velocity among the prey and the predator. Table3 shows the numerical results of the feature selection phase for both datasets. Comput. While the second half of the agents perform the following equations. Design incremental data augmentation strategy for COVID-19 CT data. Also, some image transformations were applied, such as rotation, horizontal flip, and scaling. Eng. Article In54, AlexNet pre-trained network was used to extract deep features then applied PCA to select the best features by eliminating highly correlated features. Expert Syst. The results show that, using only 6 epochs for training, the CNNs achieved very high performance on the classification task. An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm. Future Gener. Deep learning plays an important role in COVID-19 images diagnosis. The proposed IFM approach is summarized as follows: Extracting deep features from Inception, where about 51 K features were extracted. 41, 923 (2019). Szegedy, C. et al. Robertas Damasevicius. Remainder sections are organized as follows: Material and methods sectionpresents the methodology and the techniques used in this work including model structure and description. COVID-19 Chest X -Ray Image Classification with Neural Network Currently we are suffering from COVID-19, and the situation is very serious. The symbol \(r\in [0,1]\) represents a random number. Medical imaging techniques are very important for diagnosing diseases. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Simonyan, K. & Zisserman, A. Software available from tensorflow. IEEE Trans. The experimental results and comparisons with other works are presented inResults and discussion section, while they are discussed in Discussion section Finally, the conclusion is described in Conclusion section. Extensive evaluation experiments had been carried out with a collection of two public X-ray images datasets. Performance analysis of neural networks for classification of medical images with wavelets as a feature extractor. Toaar, M., Ergen, B. Med. My education and internships have equipped me with strong technical skills in Python, deep learning models, machine learning classification, text classification, and more. In9, to classify ultrasound medical images, the authors used distance-based FS methods and a Fuzzy Support Vector Machine (FSVM). While no feature selection was applied to select best features or to reduce model complexity. Computational image analysis techniques play a vital role in disease treatment and diagnosis. In general, MPA is a meta-heuristic technique that simulates the behavior of the prey and predator in nature37. To survey the hypothesis accuracy of the models. Fung, G. & Stoeckel, J. Svm feature selection for classification of spect images of alzheimers disease using spatial information. We have used RMSprop optimizer for weight updates, cross entropy loss function and selected learning rate as 0.0001. Future Gener. In the meantime, to ensure continued support, we are displaying the site without styles Covid-19 dataset. Luz, E., Silva, P.L., Silva, R. & Moreira, G. Towards an efficient deep learning model for covid-19 patterns detection in x-ray images. However, the proposed FO-MPA approach has an advantage in performance compared to other works. 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. Article Da Silva, S. F., Ribeiro, M. X., Neto, Jd. Using the best performing fine-tuned VGG-16 DTL model, tests were carried out on 470 unlabeled image dataset, which was not used in the model training and validation processes. 121, 103792 (2020). Faramarzi, A., Heidarinejad, M., Mirjalili, S. & Gandomi, A. H. Marine predators algorithm: a nature-inspired metaheuristic. Feature selection based on gaussian mixture model clustering for the classification of pulmonary nodules based on computed tomography. 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. Sahlol, A.T., Yousri, D., Ewees, A.A. et al. 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. It can be concluded that FS methods have proven their advantages in different medical imaging applications19. In this work, we have used four transfer learning models, VGG16, InceptionV3, ResNet50, and DenseNet121 for the classification tasks. Going deeper with convolutions. Appl. Our method is able to classify pneumonia from COVID-19 and visualize an abnormal area at the same time. Using X-ray images we can train a machine learning classifier to detect COVID-19 using Keras and TensorFlow. Finally, the predator follows the levy flight distribution to exploit its prey location. One from the well-know definitions of FC is the Grunwald-Letnikov (GL), which can be mathematically formulated as below40: where \(D^{\delta }(U(t))\) refers to the GL fractional derivative of order \(\delta\). Therefore, in this paper, we propose a hybrid classification approach of COVID-19. While, MPA, BPSO, SCA, and SGA obtained almost the same accuracy, followed by both bGWO, WOA, and SMA. EMRes-50 model . J. Clin. On the second dataset, dataset 2 (Fig. The parameters of each algorithm are set according to the default values. Generally, the most stable algorithms On dataset 1 are WOA, SCA, HGSO, FO-MPA, and SGA, respectively. Automatic COVID-19 lung images classification system based on convolution neural network. & Dai, Q. Discriminative clustering and feature selection for brain mri segmentation. IEEE Trans. Multimedia Tools Appl. 132, 8198 (2018). 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. The updating operation repeated until reaching the stop condition. Google Research, https://research.googleblog.com/2017/11/automl-for-large-scaleimage.html, Blog (2017). 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. Inf. Abbas, A., Abdelsamea, M.M. & Gaber, M.M. Classification of covid-19 in chest x-ray images using detrac deep convolutional neural network. Also, because COVID-19 is a virus, distinguish COVID-19 from common viral . However, the modern name is tenggiling.In Javanese it is terenggiling; and in the Philippine languages, it is goling, tanggiling, or balintong (with the same meaning).. It is obvious that such a combination between deep features and a feature selection algorithm can be efficient in several image classification tasks. 11314, 113142S (International Society for Optics and Photonics, 2020). The 1360 revised papers presented in these proceedings were carefully reviewed and selected from . The main purpose of Conv. In Eq. Stage 2: The prey/predator in this stage begin exploiting the best location that detects for their foods. Transmission scenarios for middle east respiratory syndrome coronavirus (mers-cov) and how to tell them apart. Then, applying the FO-MPA to select the relevant features from the images. 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 . Comput. Havaei, M. et al. 1. Chollet, F. Xception: Deep learning with depthwise separable convolutions. <span> <h5>Background</h5> <p>The COVID19 pandemic has precipitated global apprehensions about increased fatalities and raised concerns about gaps in healthcare . The variants of concern are Alpha, Beta, Gamma, and than the COVID-19 images. In order to normalize the values between 0 and 1 by dividing by the sum of all feature importance values, as in Eq. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. Biocybern. Figure3 illustrates the structure of the proposed IMF approach. 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). COVID-19 (coronavirus disease 2019) is a new viral infection disease that is widely spread worldwide. AMERICAN JOURNAL OF EMERGENCY MEDICINE COVID-19: Facemask use prevalence in international airports in Asia, Europe and the Americas, March 2020 is applied before larger sized kernels are applied to reduce the dimension of the channels, which accordingly, reduces the computation cost. 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). Image Anal. 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. 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