MathSciNet 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 . In our experiment, we randomly split the data into 70%, 10%, and 20% for training, validation, and testing sets, respectively. 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). This stage can be mathematically implemented as below: In Eq. Detection of lung cancer on chest ct images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. Comput. COVID-19 image classification using deep features and fractional-order marine predators algorithm. (24). The prey follows Weibull distribution during discovering the search space to detect potential locations of its food. 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. 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. Moreover, we design a weighted supervised loss that assigns higher weight for . Appl. The results of max measure (as in Eq. \(Fit_i\) denotes a fitness function value. Access through your institution. In this experiment, the selected features by FO-MPA were classified using KNN. Litjens, G. et al. A.T.S. For the image features that led to categorization, there were varied levels of agreement in the interobserver and intraobserver components that . With the help of numerous algorithms in AI, modern COVID-19 cases can be detected and managed in a classified framework. Donahue, J. et al. Key Definitions. Design incremental data augmentation strategy for COVID-19 CT data. An efficient feature generation approach based on deep learning and feature selection techniques for traffic classification. 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. 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. arXiv preprint arXiv:2003.11597 (2020). Springer Science and Business Media LLC Online. & Cmert, Z. International Conference on Machine Learning647655 (2014). 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. Syst. Acharya, U. R. et al. Using X-ray images we can train a machine learning classifier to detect COVID-19 using Keras and TensorFlow. 115, 256269 (2011). 2 (left). It also shows that FO-MPA can select the smallest subset of features, which reflects positively on performance. 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. Both the model uses Lungs CT Scan images to classify the covid-19. In general, feature selection (FS) methods are widely employed in various applications of medical imaging applications. Apostolopoulos, I. D. & Mpesiana, T. A. Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Early diagnosis, timely treatment, and proper confinement of the infected patients are some possible ways to control the spreading of . Med. layers is to extract features from input images. However, the proposed FO-MPA approach has an advantage in performance compared to other works. Imaging Syst. \(\Gamma (t)\) indicates gamma function. 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. Havaei, M. et al. 25, 3340 (2015). Feature selection based on gaussian mixture model clustering for the classification of pulmonary nodules based on computed tomography. }, \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. 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. 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). For instance,\(1\times 1\) conv. 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. MATH Furthermore, deep learning using CNN is considered one of the best choices in medical imaging applications20, especially classification. In this paper, we used two different datasets. 35, 1831 (2017). Al-qaness, M. A., Ewees, A. Remainder sections are organized as follows: Material and methods sectionpresents the methodology and the techniques used in this work including model structure and description. The evaluation showed that the RDFS improved SVM robustness against reconstruction kernel and slice thickness. . Future Gener. 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 classification accuracy of MPA, WOA, SCA, and SGA are almost the same. In our example the possible classifications are covid, normal and pneumonia. The results indicate that all CNN-based architectures outperform the ViT-based architecture in the binary classification of COVID-19 using CT images. contributed to preparing results and the final figures. A. et al. https://doi.org/10.1155/2018/3052852 (2018). Also, some image transformations were applied, such as rotation, horizontal flip, and scaling. Sci Rep 10, 15364 (2020). Softw. A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models. The variants of concern are Alpha, Beta, Gamma, and than the COVID-19 images. 121, 103792 (2020). Table3 shows the numerical results of the feature selection phase for both datasets. Eng. Imaging 35, 144157 (2015). SMA is on the second place, While HGSO, SCA, and HHO came in the third to fifth place, respectively. 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. It can be concluded that FS methods have proven their advantages in different medical imaging applications19. 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. Acharya et al.11 applied different FS methods to classify Alzheimers disease using MRI images. 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. 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). 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. Deep Learning Based Image Classification of Lungs Radiography for Detecting COVID-19 using a Deep CNN and ResNet 50 (2) To extract various textural features using the GLCM algorithm. 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. In Medical Imaging 2020: Computer-Aided Diagnosis, vol. Credit: NIAID-RML Chong, D. Y. et al. Multimedia Tools Appl. One of these datasets has both clinical and image data. In57, ResNet-50 CNN has been applied after applying horizontal flipping, random rotation, random zooming, random lighting, and random wrapping on raw images. \end{aligned} \end{aligned}$$, $$\begin{aligned} \begin{aligned} U_{i}(t+1)&= \frac{1}{1!} Future Gener. PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from . It is calculated between each feature for all classes, as in Eq. IEEE Trans. (20), \(FAD=0.2\), and W is a binary solution (0 or 1) that corresponded to random solutions. Image Underst. 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. Deep residual learning for image recognition. 2022 May;144:105350. doi: 10.1016/j.compbiomed.2022.105350. Figure3 illustrates the structure of the proposed IMF approach. Med. It classifies the chest X-ray images into three categories that includes Covid-19, Pneumonia and normal. 101, 646667 (2019). The HGSO also was ranked last. While no feature selection was applied to select best features or to reduce model complexity. 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. In 2018 IEEE International Symposium on Circuits and Systems (ISCAS), 15 (IEEE, 2018). In Future of Information and Communication Conference, 604620 (Springer, 2020). They used K-Nearest Neighbor (kNN) to classify x-ray images collected from Montgomery dataset, and it showed good performances. Epub 2022 Mar 3. However, some of the extracted features by CNN might not be sufficient, which may affect negatively the quality of the classification images. The family of coronaviruses is considered serious pathogens for people because they infect respiratory, hepatic, gastrointestinal, and neurologic diseases. Hashim, F. A., Houssein, E. H., Mabrouk, M. S., Al-Atabany, W. & Mirjalili, S. Henry gas solubility optimization: a novel physics-based algorithm. In some cases (as exists in this work), the dataset is limited, so it is not sufficient for building & training a CNN. This task is achieved by FO-MPA which randomly generates a set of solutions, each of them represents a subset of potential features. I am passionate about leveraging the power of data to solve real-world problems. Thereafter, the FO-MPA parameters are applied to update the solutions of the current population. Use of chest ct in combination with negative rt-pcr assay for the 2019 novel coronavirus but high clinical suspicion. 9, 674 (2020). 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). Fractional Differential Equations: An Introduction to Fractional Derivatives, Fdifferential Equations, to Methods of their Solution and Some of Their Applications Vol. Deep learning plays an important role in COVID-19 images diagnosis. 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. Simonyan, K. & Zisserman, A. First: prey motion based on FC the motion of the prey of Eq. Eng. 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. 69, 4661 (2014). Li, J. et al. Propose similarity regularization for improving C. COVID-19 Chest X -Ray Image Classification with Neural Network Currently we are suffering from COVID-19, and the situation is very serious. Med. Syst. For example, Lambin et al.7 proposed an efficient approach called Radiomics to extract medical image features. MRFGRO: a hybrid meta-heuristic feature selection method for screening COVID-19 using deep features, Detection and analysis of COVID-19 in medical images using deep learning techniques, Cov-caldas: A new COVID-19 chest X-Ray dataset from state of Caldas-Colombia, Deep learning in veterinary medicine, an approach based on CNN to detect pulmonary abnormalities from lateral thoracic radiographs in cats, COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images, ANFIS-Net for automatic detection of COVID-19, A multi-scale gated multi-head attention depthwise separable CNN model for recognizing COVID-19, Validating deep learning inference during chest X-ray classification for COVID-19 screening, Densely attention mechanism based network for COVID-19 detection in chest X-rays, https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/, https://github.com/ieee8023/covid-chestxray-dataset, https://stanfordmlgroup.github.io/projects/chexnet, https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia, https://www.sirm.org/en/category/articles/covid-19-database/, https://drive.google.com/file/d/1-oK-eeEgdCMCnykH364IkAK3opmqa9Rvasx/view?usp=sharing, https://doi.org/10.1016/j.irbm.2019.10.006, https://research.googleblog.com/2017/11/automl-for-large-scaleimage.html, https://doi.org/10.1016/j.engappai.2020.103662, https://www.sirm.org/category/senza-categoria/covid-19/, https://doi.org/10.1016/j.future.2020.03.055, http://creativecommons.org/licenses/by/4.0/, Skin cancer detection using ensemble of machine learning and deep learning techniques, Plastic pollution induced by the COVID-19: Environmental challenges and outlook, An Inclusive Survey on Marine Predators Algorithm: Variants andApplications, A Multi-strategy Improved Outpost and Differential Evolution Mutation Marine Predators Algorithm for Global Optimization, A light-weight convolutional Neural Network Architecture for classification of COVID-19 chest X-Ray images. Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs. Slider with three articles shown per slide. (iii) To implement machine learning classifiers for classification of COVID and non-COVID image classes. used VGG16 to classify Covid-19 and achieved good results with an accuracy of 86% [ 22 ]. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition12511258 (2017). Appl. Corona Virus lung infected X-Ray Images accessible by Kaggle a complete 590 images, which classified in 2 classes: typical and Covid-19. <span> <h5>Background</h5> <p>The COVID19 pandemic has precipitated global apprehensions about increased fatalities and raised concerns about gaps in healthcare . An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm. 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. Zhu, H., He, H., Xu, J., Fang, Q. 11314, 113142S (International Society for Optics and Photonics, 2020). In this paper, each feature selection algorithm were exposed to select the produced feature vector from Inception aiming at selecting only the most relevant features. 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. & Cmert, Z. 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. 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. Medical imaging techniques are very important for diagnosing diseases. 22, 573577 (2014). Whereas the worst one was SMA algorithm. They applied the SVM classifier for new MRI images to segment brain tumors, automatically. & 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. Brain tumor segmentation with deep neural networks. AMERICAN JOURNAL OF EMERGENCY MEDICINE COVID-19: Facemask use prevalence in international airports in Asia, Europe and the Americas, March 2020 Some people say that the virus of COVID-19 is. In the meantime, to ensure continued support, we are displaying the site without styles Huang, P. et al. For fair comparison, each algorithms was performed (run) 25 times to produce statistically stable results.The results are listed in Tables3 and4. Imag. J. Med. Methods Med. Zhang, N., Ruan, S., Lebonvallet, S., Liao, Q. The convergence behaviour of FO-MPA was evaluated over 25 independent runs and compared to other algorithms, where the x-axis and the y-axis represent the iterations and the fitness value, respectively. Health Inf. Inf. Faramarzi et al.37 divided the agents for two halves and formulated Eqs. In Dataset 2, FO-MPA also is reported as the highest classification accuracy with the best and mean measures followed by the BPSO. It is important to detect positive cases early to prevent further spread of the outbreak. (14)(15) to emulate the motion of the first half of the population (prey) and Eqs. (9) as follows. In9, to classify ultrasound medical images, the authors used distance-based FS methods and a Fuzzy Support Vector Machine (FSVM). 78, 2091320933 (2019). The first one is based on Python, where the deep neural network architecture (Inception) was built and the feature extraction part was performed. arXiv preprint arXiv:2004.07054 (2020). Cauchemez, S. et al. Computed tomography (CT) and magnetic resonance imaging (MRI) represent valuable input to AI algorithms, scanning human body sections for the sake of diagnosis. Whereas, the slowest and the insufficient convergences were reported by both SGA and WOA in Dataset 1 and by SGA in Dataset 2. 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. After applying this technique, the feature vector is minimized from 2000 to 459 and from 2000 to 462 for Dataset1 and Dataset 2, respectively. One of the main disadvantages of our approach is that its built basically within two different environments. All classication models ever, the virus mutates, and new variants emerge and dis- performed better in classifying the Non-COVID-19 images appear. Thank you for visiting nature.com. You are using a browser version with limited support for CSS. Diagnosis of parkinsons disease with a hybrid feature selection algorithm based on a discrete artificial bee colony. & Dai, Q. Discriminative clustering and feature selection for brain mri segmentation. The next process is to compute the performance of each solution using fitness value and determine which one is the best solution. and A.A.E. In Proceedings of the IEEE Conference on computer vision and pattern recognition workshops, 806813 (2014). 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 . Image Anal. 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. arXiv preprint arXiv:1409.1556 (2014). volume10, Articlenumber:15364 (2020) 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. Biocybern. 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 .

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