Decaf: A deep convolutional activation feature for generic visual recognition. 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. 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. Cancer 48, 441446 (2012). Comput. Rajpurkar, P. etal. 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. COVID-19 image classification using deep features and fractional-order marine predators algorithm Authors. Biocybern. 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. However, using medical imaging, chest CT, and chest X-ray scan can play a critical role in COVID-19 diagnosis. J. 41, 923 (2019). IEEE Trans. We can call this Task 2. PubMed Central 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). Duan, H. et al. They were manually aggregated from various web based repositories into a machine learning (ML) friendly format with accompanying data loader code. arXiv preprint arXiv:2004.05717 (2020). In this paper, we used two different datasets. Sahlol, A. T., Kollmannsberger, P. & Ewees, A. layers is to extract features from input images. Design incremental data augmentation strategy for COVID-19 CT data. The conference was held virtually due to the COVID-19 pandemic. 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. Also, they require a lot of computational resources (memory & storage) for building & training. Sci. The proposed IMF approach is employed to select only relevant and eliminate unnecessary features. Biomed. and A.A.E. 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. A.A.E. Automatic COVID-19 lung images classification system based on convolution neural network. Sahlol, A.T., Yousri, D., Ewees, A.A. et al. Expert Syst. 111, 300323. \(Fit_i\) denotes a fitness function value. }, \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. Toaar, M., Ergen, B. It also contributes to minimizing resource consumption which consequently, reduces the processing time. 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 symbol \(r\in [0,1]\) represents a random number. 69, 4661 (2014). Layers are applied to extract different types of features such as edges, texture, colors, and high-lighted patterns from the images. Dhanachandra, N. & Chanu, Y. J. A. et al. and M.A.A.A. The proposed segmentation method is capable of dealing with the problem of diffuse lung borders in CXR images of patients with COVID-19 severe or critical. 517 PDF Ensemble of Patches for COVID-19 X-Ray Image Classification Thiago Chen, G. Oliveira, Z. Dias Medicine Introduction Image Anal. For example, Lambin et al.7 proposed an efficient approach called Radiomics to extract medical image features. It is calculated between each feature for all classes, as in Eq. 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. Adv. Decis. COVID-19-X-Ray-Classification Utilizing Deep Learning to detect COVID-19 and Viral Pneumonia from x-ray images Research Publication: https://dl.acm.org/doi/10.1145/3431804 Datasets used: COVID-19 Radiography Database COVID-19 10000 Images Related Research Papers: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7187882/ 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. Refresh the page, check Medium 's site status, or find something interesting. In Future of Information and Communication Conference, 604620 (Springer, 2020). In this subsection, a comparison with relevant works is discussed. The second CNN architecture classifies the X-ray image into three classes, i.e., normal, pneumonia, and COVID-19. For more analysis of feature selection algorithms based on the number of selected features (S.F) and consuming time, Fig. Then, applying the FO-MPA to select the relevant features from the images. arXiv preprint arXiv:2003.11597 (2020). Med. 0.9875 and 0.9961 under binary and multi class classifications respectively. In this paper, different Conv. Comput. 115, 256269 (2011). Wish you all a very happy new year ! 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. 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. 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. Furthermore, the proposed GRAY+GRAY_HE+GRAY_CLAHE image representation was evaluated on two different datasets, SARS-CoV-2 CT-Scan and New_Data_CoV2, where it was found to be superior to RGB . Multimedia Tools Appl. Slider with three articles shown per slide. (18)(19) for the second half (predator) as represented below. They showed that analyzing image features resulted in more information that improved medical imaging. Fung, G. & Stoeckel, J. Svm feature selection for classification of spect images of alzheimers disease using spatial information. Using X-ray images we can train a machine learning classifier to detect COVID-19 using Keras and TensorFlow. Methods Med. Also, in12, an Fs method based on SVM was proposed to detect Alzheimers disease from SPECT images. Article Google Scholar. Extensive evaluation experiments had been carried out with a collection of two public X-ray images datasets. 35, 1831 (2017). Table2 depicts the variation in morphology of the image, lighting, structure, black spaces, shape, and zoom level among the same dataset, as well as with the other dataset. Comput. The variants of concern are Alpha, Beta, Gamma, and than the COVID-19 images. In ancient India, according to Aelian, it was . Isolation and characterization of a bat sars-like coronavirus that uses the ace2 receptor. Sci Rep 10, 15364 (2020). For example, as our input image has the shape \(224 \times 224 \times 3\), Nasnet26 produces 487 K features, Resnet25 and Xception29 produce about 100 K features and Mobilenet27 produces 50 K features, while FO-MPA produces 130 and 86 features for both dataset1 and dataset 2, respectively. Eng. 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. arXiv preprint arXiv:2003.13145 (2020). 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. Google Scholar. 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. \end{aligned}$$, $$\begin{aligned} U_i(t+1)-U_i(t)=P.R\bigotimes S_i \end{aligned}$$, $$\begin{aligned} D ^{\delta } \left[ U_{i}(t+1)\right] =P.R\bigotimes S_i \end{aligned}$$, $$D^{\delta } \left[ {U_{i} (t + 1)} \right] = U_{i} (t + 1) + \sum\limits_{{k = 1}}^{m} {\frac{{( - 1)^{k} \Gamma (\delta + 1)U_{i} (t + 1 - k)}}{{\Gamma (k + 1)\Gamma (\delta - k + 1)}}} = P \cdot R \otimes S_{i} .$$, $$\begin{aligned} \begin{aligned} U(t+1)_{i}= - \sum _{k=1}^{m} \frac{(-1)^k\Gamma (\delta +1)U_{i}(t+1-k)}{\Gamma (k+1)\Gamma (\delta -k+1)} + P.R\bigotimes S_i. A NOVEL COMPARATIVE STUDY FOR AUTOMATIC THREE-CLASS AND FOUR-CLASS COVID-19 CLASSIFICATION ON X-RAY IMAGES USING DEEP LEARNING: Authors: Yaar, H. Ceylan, M. Keywords: Convolutional neural networks Covid-19 Deep learning Densenet201 Inceptionv3 Local binary pattern Local entropy X-ray chest classification Xception: Issue Date: 2022: Publisher: While, MPA, BPSO, SCA, and SGA obtained almost the same accuracy, followed by both bGWO, WOA, and SMA. 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. I. S. of Medical Radiology. In our experiment, we randomly split the data into 70%, 10%, and 20% for training, validation, and testing sets, respectively. To further analyze the proposed algorithm, we evaluate the selected features by FO-MPA by performing classification. In this experiment, the selected features by FO-MPA were classified using KNN. Artif. In order to normalize the values between 0 and 1 by dividing by the sum of all feature importance values, as in Eq. The memory terms of the prey are updated at the end of each iteration based on first in first out concept. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. 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. & Pouladian, M. Feature selection for contour-based tuberculosis detection from chest x-ray images. https://keras.io (2015). 2 (left). Arijit Dey, Soham Chattopadhyay, Ram Sarkar, Dandi Yang, Cristhian Martinez, Jesus Carretero, Jess Alejandro Alzate-Grisales, Alejandro Mora-Rubio, Reinel Tabares-Soto, Lo Dumortier, Florent Gupin, Thomas Grenier, Linda Wang, Zhong Qiu Lin & Alexander Wong, Afnan Al-ali, Omar Elharrouss, Somaya Al-Maaddeed, Robbie Sadre, Baskaran Sundaram, Daniela Ushizima, Zahid Ullah, Muhammad Usman, Jeonghwan Gwak, Scientific Reports arXiv preprint arXiv:2004.07054 (2020). Key Definitions. Improving the ranking quality of medical image retrieval using a genetic feature selection method. In54, AlexNet pre-trained network was used to extract deep features then applied PCA to select the best features by eliminating highly correlated features. Med. The lowest accuracy was obtained by HGSO in both measures. https://doi.org/10.1016/j.future.2020.03.055 (2020). Covid-19 dataset. Keywords - Journal. For the special case of \(\delta = 1\), the definition of Eq. The evaluation confirmed that FPA based FS enhanced classification accuracy. Med. Both datasets shared some characteristics regarding the collecting sources. The Marine Predators Algorithm (MPA)is a recently developed meta-heuristic algorithm that emulates the relation among the prey and predator in nature37. Evaluate the proposed approach by performing extensive comparisons to several state-of-art feature selection algorithms, most recent CNN architectures and most recent relevant works and existing classification methods of COVID-19 images. From Fig. While55 used different CNN structures. Bukhari, S. U.K., Bukhari, S. S.K., Syed, A. 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. Average of the consuming time and the number of selected features in both datasets. In Iberian Conference on Pattern Recognition and Image Analysis, 176183 (Springer, 2011). 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. 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 . In 2019 26th National and 4th International Iranian Conference on Biomedical Engineering (ICBME), 194198 (IEEE, 2019). Also, all other works do not give further statistics about their models complexity and the number of featurset produced, unlike, our approach which extracts the most informative features (130 and 86 features for dataset 1 and dataset 2) that imply faster computation time and, accordingly, lower resource consumption. They used different images of lung nodules and breast to evaluate their FS methods. 51, 810820 (2011). Inf. 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). SMA is on the second place, While HGSO, SCA, and HHO came in the third to fifth place, respectively. Among the FS methods, the metaheuristic techniques have been established their performance overall other FS methods when applied to classify medical images. As a result, the obtained outcomes outperformed previous works in terms of the models general performance measure. Hence, the FC memory is applied during updating the prey locating in the second step of the algorithm to enhance the exploitation stage. In the current work, the values of k, and \(\zeta\) are set to 2, and 2, respectively. Kong, Y., Deng, Y. CAS PubMedGoogle Scholar. It also shows that FO-MPA can select the smallest subset of features, which reflects positively on performance. HGSO was ranked second with 146 and 87 selected features from Dataset 1 and Dataset 2, respectively. In14, the authors proposed an FS method based on a convolutional neural network (CNN) to detect pneumonia from lung X-ray images. Stage 2: The prey/predator in this stage begin exploiting the best location that detects for their foods. used a dark Covid-19 network for multiple classification experiments on Covid-19 with an accuracy of 87% [ 23 ]. Moreover, the \(R_B\) parameter has been changed to depend on weibull distribution as described below. Kharrat, A. 22, 573577 (2014). Abadi, M. et al. MathSciNet Our method is able to classify pneumonia from COVID-19 and visualize an abnormal area at the same time. Evaluation outcomes showed that GA based FS methods outperformed traditional approaches, such as filter based FS and traditional wrapper methods. Li et al.34 proposed a self-adaptive bat algorithm (BA) to address two problems in lung X-ray images, rebalancing, and feature selection. Contribute to hellorp1990/Covid-19-USF development by creating an account on GitHub. The results of max measure (as in Eq. et al. 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 . PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer. To obtain In addition, up to our knowledge, MPA has not applied to any real applications yet. 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. Moreover, the Weibull distribution employed to modify the exploration function. Syst. Google Research, https://research.googleblog.com/2017/11/automl-for-large-scaleimage.html, Blog (2017). & Zhu, Y. Kernel feature selection to fuse multi-spectral mri images for brain tumor segmentation. 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. All classication models ever, the virus mutates, and new variants emerge and dis- performed better in classifying the Non-COVID-19 images appear. While no feature selection was applied to select best features or to reduce model complexity. Future Gener. 2. Springer Science and Business Media LLC Online. https://doi.org/10.1155/2018/3052852 (2018). In this paper, we used TPUs for powerful computation, which is more appropriate for CNN. Acharya et al.11 applied different FS methods to classify Alzheimers disease using MRI images. (5). MATH The shape of the output from the Inception is (5, 5, 2048), which represents a feature vector of size 51200. 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. https://doi.org/10.1038/s41598-020-71294-2, DOI: https://doi.org/10.1038/s41598-020-71294-2. Google Scholar. Credit: NIAID-RML CAS 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. Med. 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. All authors discussed the results and wrote the manuscript together. (8) at \(T = 1\), the expression of Eq. For the image features that led to categorization, there were varied levels of agreement in the interobserver and intraobserver components that . (14)(15) to emulate the motion of the first half of the population (prey) and Eqs. 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. Nature 503, 535538 (2013). It is obvious that such a combination between deep features and a feature selection algorithm can be efficient in several image classification tasks. 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. The accuracy measure is used in the classification phase. A., Fan, H. & Abd ElAziz, M. Optimization method for forecasting confirmed cases of covid-19 in china. 132, 8198 (2018). Tree based classifier are the most popular method to calculate feature importance to improve the classification since they have high accuracy, robustness, and simple38. Corona Virus lung infected X-Ray Images accessible by Kaggle a complete 590 images, which classified in 2 classes: typical and Covid-19. Comput. In this subsection, the performance of the proposed COVID-19 classification approach is compared to other CNN architectures. Deep Learning Based Image Classification of Lungs Radiography for Detecting COVID-19 using a Deep CNN and ResNet 50 \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. D.Y. In Table4, for Dataset 1, the proposed FO-MPA approach achieved the highest accuracy in the best and mean measures, as it reached 98.7%, and 97.2% of correctly classified samples, respectively. Whereas, FO-MPA, MPA, HGSO, and WOA showed similar STD results. The Softmax activation function is used for this purpose because the output should be binary (positive COVID-19 negative COVID-19). Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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 results show that, using only 6 epochs for training, the CNNs achieved very high performance on the classification task. In this paper, filters of size 2, besides a stride of 2 and \(2 \times 2\) as Max pool, were adopted. For fair comparison, each algorithms was performed (run) 25 times to produce statistically stable results.The results are listed in Tables3 and4. (4). (14)-(15) are implemented in the first half of the agents that represent the exploitation. 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. Pool layers are used mainly to reduce the inputs size, which accelerates the computation as well. 6, right), our approach still provides an overall accuracy of 99.68%, putting it first with a slight advantage over MobileNet (99.67 %). (33)), showed that FO-MPA also achieved the best value of the fitness function compared to others. 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/]. They applied the SVM classifier for new MRI images to segment brain tumors, automatically. & Cmert, Z. The whale optimization algorithm. Besides, all algorithms showed the same statistical stability in STD measure, except for HHO and HGSO. Currently, a new coronavirus, called COVID-19, has spread to many countries, with over two million infected people or so-called confirmed cases. In some cases (as exists in this work), the dataset is limited, so it is not sufficient for building & training a CNN. The MCA-based model is used to process decomposed images for further classification with efficient storage. Negative COVID-19 images were collected from another Chest X-ray Kaggle published dataset43. The predator tries to catch the prey while the prey exploits the locations of its food. Transmission scenarios for middle east respiratory syndrome coronavirus (mers-cov) and how to tell them apart. Med. Purpose The study aimed at developing an AI . The model was developed using Keras library47 with Tensorflow backend48. COVID-19 Chest X -Ray Image Classification with Neural Network Currently we are suffering from COVID-19, and the situation is very serious. Health Inf. ADS Also, WOA algorithm showed good results in all measures, unlike dataset 1, which can conclude that no algorithm can solve all kinds of problems. contributed to preparing results and the final figures. 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. 95, 5167 (2016). These datasets contain hundreds of frontal view X-rays and considered the largest public resource for COVID-19 image data. Al-qaness, M. A., Ewees, A. 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. Netw. Our results indicate that the VGG16 method outperforms . While the second half of the agents perform the following equations. Feature selection using flower pollination optimization to diagnose lung cancer from ct images. Inception architecture is described in Fig. E. B., Traina-Jr, C. & Traina, A. J. To survey the hypothesis accuracy of the models. (23), the general formulation for the solutions of FO-MPA based on FC memory perspective can be written as follows: After checking the previous formula, it can be detected that the motion of the prey becomes based on some terms from the previous solutions with a length of (m), as depicted in Fig.
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