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Eurosurveillance 18, 20503 (2013). Google Scholar. In this work, the MPA is enhanced by fractional calculus memory feature, as a result, Fractional-order Marine Predators Algorithm (FO-MPA) is introduced. 22, 573577 (2014). 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. 92, 103662. https://doi.org/10.1016/j.engappai.2020.103662 (2020). Introduction Google Scholar. In9, to classify ultrasound medical images, the authors used distance-based FS methods and a Fuzzy Support Vector Machine (FSVM). The MCA-based model is used to process decomposed images for further classification with efficient storage. Also, because COVID-19 is a virus, distinguish COVID-19 from common viral . The family of coronaviruses is considered serious pathogens for people because they infect respiratory, hepatic, gastrointestinal, and neurologic diseases. 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. 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. The memory terms of the prey are updated at the end of each iteration based on first in first out concept. 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 data was collected mainly from retrospective cohorts of pediatric patients from Guangzhou Women and Childrens medical center. Deep learning plays an important role in COVID-19 images diagnosis. 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. Comput. The results show that, using only 6 epochs for training, the CNNs achieved very high performance on the classification task. Faramarzi, A., Heidarinejad, M., Mirjalili, S. & Gandomi, A. H. Marine predators algorithm: a nature-inspired metaheuristic. 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. Remainder sections are organized as follows: Material and methods sectionpresents the methodology and the techniques used in this work including model structure and description. 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. 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. Luz, E., Silva, P.L., Silva, R. & Moreira, G. Towards an efficient deep learning model for covid-19 patterns detection in x-ray images. (22) can be written as follows: By taking into account the early mentioned relation in Eq. Comput. To obtain Software available from tensorflow. In 2018 IEEE International Symposium on Circuits and Systems (ISCAS), 15 (IEEE, 2018). In 2019 26th National and 4th International Iranian Conference on Biomedical Engineering (ICBME), 194198 (IEEE, 2019). & Mirjalili, S. Slime mould algorithm: A new method for stochastic optimization. Future Gener. Adv. https://doi.org/10.1155/2018/3052852 (2018). For Dataset 2, FO-MPA showed acceptable (not the best) performance, as it achieved slightly similar results to the first and second ranked algorithm (i.e., MPA and SMA) on mean, best, max, and STD measures. Moreover, other COVID-19 positive images were added by the Italian Society of Medical and Interventional Radiology (SIRM) COVID-19 Database45. Methods Med. PubMedGoogle Scholar. (iii) To implement machine learning classifiers for classification of COVID and non-COVID image classes. They employed partial differential equations for extracting texture features of medical images. This dataset consists of 219 COVID-19 positive images and 1341 negative COVID-19 images. Automated detection of covid-19 cases using deep neural networks with x-ray images. Comput. It is obvious that such a combination between deep features and a feature selection algorithm can be efficient in several image classification tasks. Lett. Expert Syst. You are using a browser version with limited support for CSS. all above stages are repeated until the termination criteria is satisfied. Blog, G. Automl for large scale image classification and object detection. A properly trained CNN requires a lot of data and CPU/GPU time. Stage 2: The prey/predator in this stage begin exploiting the best location that detects for their foods. 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. 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. Furthermore, deep learning using CNN is considered one of the best choices in medical imaging applications20, especially classification. \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. Radiology 295, 2223 (2020). 517 PDF Ensemble of Patches for COVID-19 X-Ray Image Classification Thiago Chen, G. Oliveira, Z. Dias Medicine FCM reinforces the ANFIS classification learning phase based on the features of COVID-19 patients. CAS Key Definitions. and A.A.E. Da Silva, S. F., Ribeiro, M. X., Neto, Jd. Appl. 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. Also, in12, an Fs method based on SVM was proposed to detect Alzheimers disease from SPECT images. Eng. 41, 923 (2019). The optimum path forest (OPF) classifier was applied to classify pulmonary nodules based on CT images. COVID 19 X-ray image classification. A. Regarding the consuming time as in Fig. 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 . implemented the deep neural networks and classification as well as prepared the related figures and manuscript text. The predator uses the Weibull distribution to improve the exploration capability. & Zhu, Y. Kernel feature selection to fuse multi-spectral mri images for brain tumor segmentation. 6, right), our approach still provides an overall accuracy of 99.68%, putting it first with a slight advantage over MobileNet (99.67 %). 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. Bibliographic details on CECT: Controllable Ensemble CNN and Transformer for COVID-19 image classification by capturing both local and global image features. For the image features that led to categorization, there were varied levels of agreement in the interobserver and intraobserver components that . Chowdhury, M.E. etal. J. Med. In this paper, we try to integrate deep transfer-learning-based methods, along with a convolutional block attention module (CBAM), to focus on the relevant portion of the feature maps to conduct an image-based classification of human monkeypox disease. The predator tries to catch the prey while the prey exploits the locations of its food. AMERICAN JOURNAL OF EMERGENCY MEDICINE COVID-19: Facemask use prevalence in international airports in Asia, Europe and the Americas, March 2020 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). The whale optimization algorithm. Appl. Zhang et al.16 proposed a kernel feature selection method to segment brain tumors from MRI images. Coronavirus Disease (COVID-19): A primer for emergency physicians (2020) Summer Chavez et al. The prey follows Weibull distribution during discovering the search space to detect potential locations of its food. volume10, Articlenumber:15364 (2020) Ozturk, T. et al. They also used the SVM to classify lung CT images. Going deeper with convolutions. They showed that analyzing image features resulted in more information that improved medical imaging. Johnson et al.31 applied the flower pollination algorithm (FPA) to select features from CT images of the lung, to detect lung cancers. Moreover, the \(R_B\) parameter has been changed to depend on weibull distribution as described below. In this subsection, the performance of the proposed COVID-19 classification approach is compared to other CNN architectures. medRxiv (2020). Harris hawks optimization: algorithm and applications. PubMed So, transfer learning is applied by transferring weights that were already learned and reserved into the structure of the pre-trained model, such as Inception, in this paper. 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. Med. 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).