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Biocybern. is applied before larger sized kernels are applied to reduce the dimension of the channels, which accordingly, reduces the computation cost. Toaar, M., Ergen, B. medRxiv (2020). IEEE Signal Process. & 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. Acharya et al.11 applied different FS methods to classify Alzheimers disease using MRI images. On the second dataset, dataset 2 (Fig. Syst. The combination of Conv. According to the promising results of the proposed model, that combines CNN as a feature extractor and FO-MPA as a feature selector could be useful and might be successful in being applied in other image classification tasks. Comput. (22) can be written as follows: By taking into account the early mentioned relation in Eq. Robustness-driven feature selection in classification of fibrotic interstitial lung disease patterns in computed tomography using 3d texture features. Both the model uses Lungs CT Scan images to classify the covid-19. A. Appl. Inception architecture is described in Fig. Expert Syst. COVID-19 image classification using deep features and fractional-order marine predators algorithm, $$\begin{aligned} \chi ^2=\sum _{k=1}^{n} \frac{(O_k - E_k)^2}{E_k} \end{aligned}$$, $$\begin{aligned} ni_{j}=w_{j}C_{j}-w_{left(j)}C_{left(j)}-w_{right(j)}C_{right(j)} \end{aligned}$$, $$\begin{aligned} fi_{i}=\frac{\sum _{j:node \mathbf \ {j} \ splits \ on \ feature \ i}ni_{j}}{\sum _{{k}\in all \ nodes }ni_{k}} \end{aligned}$$, $$\begin{aligned} normfi_{i}=\frac{fi_{i}}{\sum _{{j}\in all \ nodes }fi_{j}} \end{aligned}$$, $$\begin{aligned} REfi_{i}=\frac{\sum _{j \in all trees} normfi_{ij}}{T} \end{aligned}$$, $$\begin{aligned} D^{\delta }(U(t))=\lim \limits _{h \rightarrow 0} \frac{1}{h^\delta } \sum _{k=0}^{\infty }(-1)^{k} \begin{pmatrix} \delta \\ k\end{pmatrix} U(t-kh), \end{aligned}$$, $$\begin{aligned} \begin{pmatrix} \delta \\ k \end{pmatrix}= \frac{\Gamma (\delta +1)}{\Gamma (k+1)\Gamma (\delta -k+1)}= \frac{\delta (\delta -1)(\delta -2)\ldots (\delta -k+1)}{k! In general, feature selection (FS) methods are widely employed in various applications of medical imaging applications. et al. 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. chest X-ray images into three classes of COVID-19, normal chest X-ray and other lung diseases. Jcs: An explainable covid-19 diagnosis system by joint classification and segmentation. Objective: Lung image classification-assisted diagnosis has a large application market. Zhang et al.16 proposed a kernel feature selection method to segment brain tumors from MRI images. Yousri, D. & Mirjalili, S. Fractional-order cuckoo search algorithm for parameter identification of the fractional-order chaotic, chaotic with noise and hyper-chaotic financial systems. Recombinant: A process in which the genomes of two SARS-CoV-2 variants (that have infected a person at the same time) combine during the viral replication process to form a new variant that is different . Comparison with other previous works using accuracy measure. Radiomics: extracting more information from medical images using advanced feature analysis. 2 (right). Sign up for the Nature Briefing newsletter what matters in science, free to your inbox daily. In ancient India, according to Aelian, it was . Knowl. This algorithm is tested over a global optimization problem. As seen in Table3, on Dataset 1, the FO-MPA outperformed the other algorithms in the mean of fitness value as it achieved the smallest average fitness function value followed by SMA, HHO, HGSO, SCA, BGWO, MPA, and BPSO, respectively whereas, the SGA and WOA showed the worst results. implemented the deep neural networks and classification as well as prepared the related figures and manuscript text. 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. Finally, the predator follows the levy flight distribution to exploit its prey location. In57, ResNet-50 CNN has been applied after applying horizontal flipping, random rotation, random zooming, random lighting, and random wrapping on raw images. They shared some parameters, such as the total number of iterations and the number of agents which were set to 20 and 15, respectively. In order to normalize the values between 0 and 1 by dividing by the sum of all feature importance values, as in Eq. Szegedy, C. et al. Article After applying this technique, the feature vector is minimized from 2000 to 459 and from 2000 to 462 for Dataset1 and Dataset 2, respectively. 115, 256269 (2011). 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. The test accuracy obtained for the model was 98%. 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. The parameters of each algorithm are set according to the default values. Stage 2 has been executed in the second third of the total number of iterations when \(\frac{1}{3}t_{max}< t< \frac{2}{3}t_{max}\). In Dataset 2, FO-MPA also is reported as the highest classification accuracy with the best and mean measures followed by the BPSO. arXiv preprint arXiv:2003.13145 (2020). It is calculated between each feature for all classes, as in Eq. Figure7 shows the most recent published works as in54,55,56,57 and44 on both dataset 1 and dataset 2. Remainder sections are organized as follows: Material and methods sectionpresents the methodology and the techniques used in this work including model structure and description. <span> <h5>Background</h5> <p>The COVID19 pandemic has precipitated global apprehensions about increased fatalities and raised concerns about gaps in healthcare . Moreover, the Weibull distribution employed to modify the exploration function. (24). Computed tomography (CT) and magnetic resonance imaging (MRI) represent valuable input to AI algorithms, scanning human body sections for the sake of diagnosis. Layers are applied to extract different types of features such as edges, texture, colors, and high-lighted patterns from the images. 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). what medical images are commonly used for COVID-19 classification and what are the methods for COVID-19 classification. Book Fractional Differential Equations: An Introduction to Fractional Derivatives, Fdifferential Equations, to Methods of their Solution and Some of Their Applications Vol. A.T.S. A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models. Regarding the consuming time as in Fig. 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. 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. arXiv preprint arXiv:1704.04861 (2017). I am passionate about leveraging the power of data to solve real-world problems. & Carlsson, S. Cnn features off-the-shelf: an astounding baseline for recognition. For the special case of \(\delta = 1\), the definition of Eq. Med. In this paper, we apply a convolutional neural network (CNN) to extract features from COVID-19 X-Ray images. They showed that analyzing image features resulted in more information that improved medical imaging. Moreover, the \(R_B\) parameter has been changed to depend on weibull distribution as described below. While the second half of the agents perform the following equations. The main purpose of Conv. 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. For general case based on the FC definition, the Eq. Softw. Chowdhury, M.E. etal. 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). Decis. wrote the intro, related works and prepare results. On January 20, 2023, Japanese Prime Minister Fumio Kishida announced that the country would be downgrading the COVID-19 classification. Whereas, the worst algorithm was BPSO. \(r_1\) and \(r_2\) are the random index of the prey. Huang, P. et al. Detection of lung cancer on chest ct images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. Med. For more analysis of feature selection algorithms based on the number of selected features (S.F) and consuming time, Fig. It is obvious that such a combination between deep features and a feature selection algorithm can be efficient in several image classification tasks. 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 . MathSciNet COVID 19 X-ray image classification. Then, using an enhanced version of Marine Predators Algorithm to select only relevant features. Automatic CNN-based Chest X-Ray (CXR) image classification for detecting Covid-19 attracted so much attention. used a dark Covid-19 network for multiple classification experiments on Covid-19 with an accuracy of 87% [ 23 ]. Image Underst. 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. AMERICAN JOURNAL OF EMERGENCY MEDICINE COVID-19: Facemask use prevalence in international airports in Asia, Europe and the Americas, March 2020 Stage 2: The prey/predator in this stage begin exploiting the best location that detects for their foods. In transfer learning, a CNN which was previously trained on a large & diverse image dataset can be applied to perform a specific classification task by23. They applied a fuzzy decision tree classifier, and they found that fuzzy PSO improved the classification accuracy. However, it has some limitations that affect its quality. Mirjalili, S., Mirjalili, S. M. & Lewis, A. Grey wolf optimizer. (4). However, the proposed IMF approach achieved the best results among the compared algorithms in least time. Harris hawks optimization: algorithm and applications. Credit: NIAID-RML PubMed Initialize solutions for the prey and predator. and A.A.E. We can call this Task 2. 2022 May;144:105350. doi: 10.1016/j.compbiomed.2022.105350. So, there might be sometimes some conflict issues regarding the features vector file types or issues related to storage capacity and file transferring. For instance,\(1\times 1\) conv. Bukhari, S. U.K., Bukhari, S. S.K., Syed, A. Pool layers are used mainly to reduce the inputs size, which accelerates the computation as well. Propose similarity regularization for improving C. Nature 503, 535538 (2013). 517 PDF Ensemble of Patches for COVID-19 X-Ray Image Classification Thiago Chen, G. Oliveira, Z. Dias Medicine An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm. Multimedia Tools Appl. In9, to classify ultrasound medical images, the authors used distance-based FS methods and a Fuzzy Support Vector Machine (FSVM). }, \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.