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). Narayanan, S.J., Soundrapandiyan, R., Perumal, B. Memory FC prospective concept (left) and weibull distribution (right). For more analysis of feature selection algorithms based on the number of selected features (S.F) and consuming time, Fig. and A.A.E. 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). J. The shape of the output from the Inception is (5, 5, 2048), which represents a feature vector of size 51200. These images have been further used for the classification of COVID-19 and non-COVID-19 images using ResNet50 and AlexNet convolutional neural network (CNN) models. Our method is able to classify pneumonia from COVID-19 and visualize an abnormal area at the same time. Deep learning plays an important role in COVID-19 images diagnosis. implemented the FO-MPA swarm optimization and prepared the related figures and manuscript text. In general, MPA is a meta-heuristic technique that simulates the behavior of the prey and predator in nature37. Imaging Syst. 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. Image Anal. Tensorflow: Large-scale machine learning on heterogeneous systems, 2015. Netw. Correspondence to The largest features were selected by SMA and SGA, respectively. 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. Harikumar, R. & Vinoth Kumar, B. In ancient India, according to Aelian, it was . Future Gener. Donahue, J. et al. 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. The accuracy measure is used in the classification phase. A.T.S. Bibliographic details on CECT: Controllable Ensemble CNN and Transformer for COVID-19 image classification by capturing both local and global image features. 1. & Wang, W. Medical image segmentation using fruit fly optimization and density peaks clustering. 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. So, for a \(4 \times 4\) matrix, will result in \(2 \times 2\) matrix after applying max pooling. Finally, the predator follows the levy flight distribution to exploit its prey location. CAS The parameters of each algorithm are set according to the default values. It classifies the chest X-ray images into three categories that includes Covid-19, Pneumonia and normal. Such methods might play a significant role as a computer-aided tool for image-based clinical diagnosis soon. Comput. The memory terms of the prey are updated at the end of each iteration based on first in first out concept. Multimedia Tools Appl. 4 and Table4 list these results for all algorithms. The updating operation repeated until reaching the stop condition. Deep Learning Based Image Classification of Lungs Radiography for Detecting COVID-19 using a Deep CNN and ResNet 50 D.Y. Experimental results have shown that the proposed Fuzzy Gabor-CNN algorithm attains highest accuracy, Precision, Recall and F1-score when compared to existing feature extraction and classification techniques. Zhang et al.16 proposed a kernel feature selection method to segment brain tumors from MRI images. 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. 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). Zhu, H., He, H., Xu, J., Fang, Q. J. Clin. COVID 19 X-ray image classification. Diagnosis of parkinsons disease with a hybrid feature selection algorithm based on a discrete artificial bee colony. Dhanachandra and Chanu35 proposed a hybrid method of dynamic PSO and fuzzy c-means to segment two types of medical images, MRI and synthetic images. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. Med. 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 . 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. arXiv preprint arXiv:2003.13815 (2020). & Cao, J. Currently, a new coronavirus, called COVID-19, has spread to many countries, with over two million infected people or so-called confirmed cases. Use of chest ct in combination with negative rt-pcr assay for the 2019 novel coronavirus but high clinical suspicion. Eng. 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 . Layers are applied to extract different types of features such as edges, texture, colors, and high-lighted patterns from the images. According to the best measure, the FO-MPA performed similarly to the HHO algorithm, followed by SMA, HGSO, and SCA, respectively. Stage 3: This stage executed on the last third of the iteration numbers (\(t>\frac{2}{3}t_{max}\)) where based on the following formula: Eddy formation and Fish Aggregating Devices effect: Faramarzi et al.37 considered the external impacts from the environment, such as the eddy formation or Fish Aggregating Devices (FADs) effects to avoid the local optimum solutions. A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models. medRxiv (2020). (2) To extract various textural features using the GLCM algorithm. Also, As seen in Fig. 2022 May;144:105350. doi: 10.1016/j.compbiomed.2022.105350. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. Image Classification With ResNet50 Convolution Neural Network (CNN) on Covid-19 Radiography | by Emmanuella Anggi | The Startup | Medium 500 Apologies, but something went wrong on our end.. 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 Extensive comparisons had been implemented to compare the FO-MPA with several feature selection algorithms, including SMA, HHO, HGSO, WOA, SCA, bGWO, SGA, BPSO, besides the classic MPA. 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. IRBM https://doi.org/10.1016/j.irbm.2019.10.006 (2019). 2 (left). Classification of COVID-19 X-ray images with Keras and its potential problem | by Yiwen Lai | Analytics Vidhya | Medium Write Sign up 500 Apologies, but something went wrong on our end.. HIGHLIGHTS who: Yuan Jian and Qin Xiao from the Fukuoka University, Japan have published the Article: Research and Application of Fine-Grained Image Classification Based on Small Collar Dataset, in the Journal: (JOURNAL) what: MC-Loss drills down on the channels to effectively navigate the model, focusing on different distinguishing regions and highlighting diverse features. FC provides a clear interpretation of the memory and hereditary features of the process. However, the proposed FO-MPA approach has an advantage in performance compared to other works. 7, most works are pre-prints for two main reasons; COVID-19 is the most recent and trend topic; also, there are no sufficient datasets that can be used for reliable results. So, based on this motivation, we apply MPA as a feature selector from deep features that produced from CNN (largely redundant), which, accordingly minimize capacity and resources consumption and can improve the classification of COVID-19 X-ray images. Google Scholar. 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 . 0.9875 and 0.9961 under binary and multi class classifications respectively. 35, 1831 (2017). Shi, H., Li, H., Zhang, D., Cheng, C. & Cao, X. 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. 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. 41, 923 (2019). 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. It can be concluded that FS methods have proven their advantages in different medical imaging applications19. Also, WOA algorithm showed good results in all measures, unlike dataset 1, which can conclude that no algorithm can solve all kinds of problems. Pool layers are used mainly to reduce the inputs size, which accelerates the computation as well. Syst. Comput. Image Anal. The . (14)(15) to emulate the motion of the first half of the population (prey) and Eqs. Number of extracted feature and classification accuracy by FO-MPA compared to other CNNs on dataset 1 (left) and on dataset 2 (right). Sahlol, A. T., Kollmannsberger, P. & Ewees, A. Int. 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. In Eq. 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. PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer. For each decision tree, node importance is calculated using Gini importance, Eq. Software available from tensorflow. Toaar, M., Ergen, B. Slider with three articles shown per slide. While55 used different CNN structures. 97, 849872 (2019). 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). Comput. As a result, the obtained outcomes outperformed previous works in terms of the models general performance measure. Therefore, in this paper, we propose a hybrid classification approach of COVID-19. In Inception, there are different sizes scales convolutions (conv. Remainder sections are organized as follows: Material and methods sectionpresents the methodology and the techniques used in this work including model structure and description. A combination of fractional-order and marine predators algorithm (FO-MPA) is considered an integration among a robust tool in mathematics named fractional-order calculus (FO). 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. Deep residual learning for image recognition. Besides, the binary classification between two classes of COVID-19 and normal chest X-ray is proposed. Syst. However, some of the extracted features by CNN might not be sufficient, which may affect negatively the quality of the classification images. org (2015). On the second dataset, dataset 2 (Fig. Appl. All classication models ever, the virus mutates, and new variants emerge and dis- performed better in classifying the Non-COVID-19 images appear. Two real datasets about COVID-19 patients are studied in this paper. Zhang, N., Ruan, S., Lebonvallet, S., Liao, Q. Chollet, F. Xception: Deep learning with depthwise separable convolutions. 22, 573577 (2014). Google Research, https://research.googleblog.com/2017/11/automl-for-large-scaleimage.html, Blog (2017). Med. Can ai help in screening viral and covid-19 pneumonia? 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. Simonyan, K. & Zisserman, A. Duan, H. et al. Eur. Da Silva, S. F., Ribeiro, M. X., Neto, Jd. While no feature selection was applied to select best features or to reduce model complexity. They also used the SVM to classify lung CT images. Mirjalili, S., Mirjalili, S. M. & Lewis, A. Grey wolf optimizer. Thank you for visiting nature.com. 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. SMA is on the second place, While HGSO, SCA, and HHO came in the third to fifth place, respectively. 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. Med. ISSN 2045-2322 (online). Continuing on my commitment to share small but interesting things in Google Cloud, this time I created a model for a Some people say that the virus of COVID-19 is. 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. Internet Explorer). M.A.E. A. Deep cnns for microscopic image classification by exploiting transfer learning and feature concatenation. This algorithm is tested over a global optimization problem. 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. & Cmert, Z. Accordingly, that reflects on efficient usage of memory, and less resource consumption. The combination of Conv. Syst. Inspired by this concept, Faramarzi et al.37 developed the MPA algorithm by considering both of a predator a prey as solutions. . 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. Kong, Y., Deng, Y. \delta U_{i}(t)+ \frac{1}{2! 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. Generally, the proposed FO-MPA approach showed satisfying performance in both the feature selection ratio and the classification rate. This paper reviews the recent progress of deep learning in COVID-19 images applications from five aspects; Firstly, 33 COVID-19 datasets and data enhancement methods are introduced; Secondly, COVID-19 classification methods . Brain tumor segmentation with deep neural networks. https://doi.org/10.1038/s41598-020-71294-2, DOI: https://doi.org/10.1038/s41598-020-71294-2. Computed tomography (CT) and magnetic resonance imaging (MRI) represent valuable input to AI algorithms, scanning human body sections for the sake of diagnosis. (iii) To implement machine learning classifiers for classification of COVID and non-COVID image classes. 152, 113377 (2020). The main purpose of Conv. Syst. (3), the importance of each feature is then calculated. 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. Med. For the image features that led to categorization, there were varied levels of agreement in the interobserver and intraobserver components that . In54, AlexNet pre-trained network was used to extract deep features then applied PCA to select the best features by eliminating highly correlated features. & SHAH, S. S.H. The diagnostic evaluation of convolutional neural network (cnn) for the assessment of chest x-ray of patients infected with covid-19. This means we can use pre-trained model weights, leveraging all of the time and data it took for training the convolutional part, and just remove the FCN layer. Highlights COVID-19 CT classification using chest tomography (CT) images. IEEE Trans. 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). Comparison with other previous works using accuracy measure. The proposed approach was evaluated on two public COVID-19 X-ray datasets which achieves both high performance and reduction of computational complexity. 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/]. 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. Afzali, A., Mofrad, F.B. MathSciNet A properly trained CNN requires a lot of data and CPU/GPU time. Google Scholar. (22) can be written as follows: By taking into account the early mentioned relation in Eq. Metric learning Metric learning can create a space in which image features within the. The family of coronaviruses is considered serious pathogens for people because they infect respiratory, hepatic, gastrointestinal, and neurologic diseases. PubMedGoogle Scholar. Health Inf. The results show that, using only 6 epochs for training, the CNNs achieved very high performance on the classification task. Heidari, A. \(Fit_i\) denotes a fitness function value. 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. Figure5, shows that FO-MPA shows an efficient and faster convergence than the other optimization algorithms on both datasets. IEEE Signal Process. Dual feature selection and rebalancing strategy using metaheuristic optimization algorithms in x-ray image datasets. From Fig. Eng. In this subsection, the performance of the proposed COVID-19 classification approach is compared to other CNN architectures. So some statistical operations have been added to exclude irrelevant and noisy features, and by making it more computationally efficient and stable, they are summarized as follows: Chi-square is applied to remove the features which have a high correlation values by computing the dependence between them. A survey on deep learning in medical image analysis. TOKYO, Jan 26 (Reuters) - Japan is set to downgrade its classification of COVID-19 to that of a less serious disease on May 8, revising its measures against the coronavirus such as relaxing. 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. Eng. Imaging 35, 144157 (2015). (20), \(FAD=0.2\), and W is a binary solution (0 or 1) that corresponded to random solutions. Article We can call this Task 2. The results of max measure (as in Eq. Purpose The study aimed at developing an AI . Cauchemez, S. et al. There are three main parameters for pooling, Filter size, Stride, and Max pool. Coronavirus Disease (COVID-19): A primer for emergency physicians (2020) Summer Chavez et al. In this subsection, a comparison with relevant works is discussed. (24). In this paper, after applying Chi-square, the feature vector is minimized for both datasets from 51200 to 2000. Cite this article. While, MPA, BPSO, SCA, and SGA obtained almost the same accuracy, followed by both bGWO, WOA, and SMA. Eurosurveillance 18, 20503 (2013). Design incremental data augmentation strategy for COVID-19 CT data. In14, the authors proposed an FS method based on a convolutional neural network (CNN) to detect pneumonia from lung X-ray images. wrote the intro, related works and prepare results. AMERICAN JOURNAL OF EMERGENCY MEDICINE COVID-19: Facemask use prevalence in international airports in Asia, Europe and the Americas, March 2020 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. Nguyen, L.D., Lin, D., Lin, Z. Eng. Therefore, several pre-trained models have won many international image classification competitions such as VGGNet24, Resnet25, Nasnet26, Mobilenet27, Inception28 and Xception29. Automated detection of alzheimers disease using brain mri imagesa study with various feature extraction techniques. Therefore, a feature selection technique can be applied to perform this task by removing those irrelevant features. Regarding the consuming time as in Fig. In the current work, the values of k, and \(\zeta\) are set to 2, and 2, respectively. After feature extraction, we applied FO-MPA to select the most significant features. IEEE Trans. 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. The HGSO also was ranked last. 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. 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 \(\delta\). Etymology. Recently, a combination between the fractional calculus tool and the meta-heuristics opens new doors in providing robust and reliable variants41. the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Cohen, J.P., Morrison, P. & Dao, L. Covid-19 image data collection. In this experiment, the selected features by FO-MPA were classified using KNN. et al. 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. Abadi, M. et al. 25, 3340 (2015). Using X-ray images we can train a machine learning classifier to detect COVID-19 using Keras and TensorFlow. The variants of concern are Alpha, Beta, Gamma, and than the COVID-19 images.

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