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Garda Negara Wisnumurti - Bojonegoro, Jawa Timur, Indonesia | Profil 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. The test accuracy obtained for the model was 98%. 152, 113377 (2020). Covid-19-USF/test.py at master hellorp1990/Covid-19-USF The results are the best achieved on these datasets when compared to a set of recent feature selection algorithms. AMERICAN JOURNAL OF EMERGENCY MEDICINE COVID-19: Facemask use prevalence in international airports in Asia, Europe and the Americas, March 2020 121, 103792 (2020). In this paper, after applying Chi-square, the feature vector is minimized for both datasets from 51200 to 2000. HIGHLIGHTS who: Qinghua Xie and colleagues from the Te Afliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, China have published the Article: Automatic Segmentation and Classification for Antinuclear Antibody Images Based on Deep Learning, in the Journal: Computational Intelligence and Neuroscience of 14/08/2022 what: Terefore, the authors . Acharya et al.11 applied different FS methods to classify Alzheimers disease using MRI images. (22) can be written as follows: By taking into account the early mentioned relation in Eq. Li, H. etal. In Smart Intelligent Computing and Applications, 305313 (Springer, 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. J. 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. Robustness-driven feature selection in classification of fibrotic interstitial lung disease patterns in computed tomography using 3d texture features. https://doi.org/10.1016/j.future.2020.03.055 (2020). While the second half of the agents perform the following equations. 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 . The evaluation confirmed that FPA based FS enhanced classification accuracy. 2020-09-21 . & Baby, C.J. Emphysema medical image classification using fuzzy decision tree with fuzzy particle swarm optimization clustering. Table2 shows some samples from two datasets. The symbol \(r\in [0,1]\) represents a random number. Deep Learning Based Image Classification of Lungs Radiography for Detecting COVID-19 using a Deep CNN and ResNet 50 Chowdhury, M.E. etal. Lung Cancer Classification Model Using Convolution Neural Network While no feature selection was applied to select best features or to reduce model complexity. IRBM https://doi.org/10.1016/j.irbm.2019.10.006 (2019). (18)(19) for the second half (predator) as represented below. Med. Methods: We employed a public dataset acquired from 20 COVID-19 patients, which . Da Silva, S. F., Ribeiro, M. X., Neto, Jd. Covid-19 dataset. is applied before larger sized kernels are applied to reduce the dimension of the channels, which accordingly, reduces the computation cost. Provided by the Springer Nature SharedIt content-sharing initiative, Environmental Science and Pollution Research (2023), Archives of Computational Methods in Engineering (2023), Arabian Journal for Science and Engineering (2023). Comput. Also, image segmentation can extract critical features, including the shape of tissues, and texture5,6. Identifying Facemask-Wearing Condition Using Image Super-Resolution 78, 2091320933 (2019). It classifies the chest X-ray images into three categories that includes Covid-19, Pneumonia and normal. Authors He, K., Zhang, X., Ren, S. & Sun, J. MathSciNet According to the formula10, the initial locations of the prey and predator can be defined as below: where the Elite matrix refers to the fittest predators. 2 (left). 115, 256269 (2011). Harikumar, R. & Vinoth Kumar, B. Four measures for the proposed method and the compared algorithms are listed. The classification accuracy of MPA, WOA, SCA, and SGA are almost the same. In this paper, Inception is applied as a feature extractor, where the input image shape is (229, 229, 3). Stage 1: After the initialization, the exploration phase is implemented to discover the search space. 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. They shared some parameters, such as the total number of iterations and the number of agents which were set to 20 and 15, respectively. Correspondence to In general, feature selection (FS) methods are widely employed in various applications of medical imaging applications. SMA is on the second place, While HGSO, SCA, and HHO came in the third to fifth place, respectively. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Eurosurveillance 18, 20503 (2013). PDF Classification of Covid-19 and Other Lung Diseases From Chest X-ray Images Classification of COVID19 using Chest X-ray Images in Keras - Coursera and pool layers, three fully connected layers, the last one performs classification. Medical imaging techniques are very important for diagnosing diseases. Sci. Moreover, the Weibull distribution employed to modify the exploration function. For more analysis of feature selection algorithms based on the number of selected features (S.F) and consuming time, Fig. 25, 3340 (2015). This algorithm is tested over a global optimization problem. Besides, the binary classification between two classes of COVID-19 and normal chest X-ray is proposed. & 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. \(\Gamma (t)\) indicates gamma function. Evaluation outcomes showed that GA based FS methods outperformed traditional approaches, such as filter based FS and traditional wrapper methods. FCM reinforces the ANFIS classification learning phase based on the features of COVID-19 patients. IEEE Signal Process. The Marine Predators Algorithm (MPA)is a recently developed meta-heuristic algorithm that emulates the relation among the prey and predator in nature37. Softw. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 19 (2015). Johnson et al.31 applied the flower pollination algorithm (FPA) to select features from CT images of the lung, to detect lung cancers. }, \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. Image Anal. The results show that, using only 6 epochs for training, the CNNs achieved very high performance on the classification task. 2 (right). Ge, X.-Y. SharifRazavian, A., Azizpour, H., Sullivan, J. Springer Science and Business Media LLC Online. Tree based classifier are the most popular method to calculate feature importance to improve the classification since they have high accuracy, robustness, and simple38. The results are the best achieved compared to other CNN architectures and all published works in the same datasets. Abbas, A., Abdelsamea, M.M. & Gaber, M.M. Classification of covid-19 in chest x-ray images using detrac deep convolutional neural network. 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. For example, Lambin et al.7 proposed an efficient approach called Radiomics to extract medical image features. Transmission scenarios for middle east respiratory syndrome coronavirus (mers-cov) and how to tell them apart. Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. https://doi.org/10.1155/2018/3052852 (2018). Finally, the predator follows the levy flight distribution to exploit its prey location. New Images of Novel Coronavirus SARS-CoV-2 Now Available NIAID Now | February 13, 2020 This scanning electron microscope image shows SARS-CoV-2 (yellow)also known as 2019-nCoV, the virus that causes COVID-19isolated from a patient in the U.S., emerging from the surface of cells (blue/pink) cultured in the lab. Multi-domain medical image translation generation for lung image and A.A.E. For both datasets, the Covid19 images were collected from patients with ages ranging from 40-84 from both genders. Therefore, several pre-trained models have won many international image classification competitions such as VGGNet24, Resnet25, Nasnet26, Mobilenet27, Inception28 and Xception29. (20), \(FAD=0.2\), and W is a binary solution (0 or 1) that corresponded to random solutions. 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. First: prey motion based on FC the motion of the prey of Eq. COVID-19 Detection via Image Classification using Deep Learning on Decis. 95, 5167 (2016). Imaging 35, 144157 (2015). 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. Computer Vision - ECCV 2020 16th European Conference, Glasgow, UK The results indicate that all CNN-based architectures outperform the ViT-based architecture in the binary classification of COVID-19 using CT images. They employed partial differential equations for extracting texture features of medical images. \(\bigotimes\) indicates the process of element-wise multiplications. Also, some image transformations were applied, such as rotation, horizontal flip, and scaling. Luz, E., Silva, P.L., Silva, R. & Moreira, G. Towards an efficient deep learning model for covid-19 patterns detection in x-ray images. For the image features that led to categorization, there were varied levels of agreement in the interobserver and intraobserver components that . Computational image analysis techniques play a vital role in disease treatment and diagnosis. Toaar, M., Ergen, B. COVID-19 (coronavirus disease 2019) is a new viral infection disease that is widely spread worldwide. COVID-19 Chest X -Ray Image Classification with Neural Network Currently we are suffering from COVID-19, and the situation is very serious. However, the modern name is tenggiling.In Javanese it is terenggiling; and in the Philippine languages, it is goling, tanggiling, or balintong (with the same meaning).. Machine-learning classification of texture features of portable chest X Appl. It is important to detect positive cases early to prevent further spread of the outbreak. Lambin, P. et al. 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. This task is achieved by FO-MPA which randomly generates a set of solutions, each of them represents a subset of potential features.