(33)), showed that FO-MPA also achieved the best value of the fitness function compared to others. Therefore, a feature selection technique can be applied to perform this task by removing those irrelevant features. Donahue, J. et al. kharrat and Mahmoud32proposed an FS method based on a hybrid of Simulated Annealing (SA) and GA to classify brain tumors using MRI. layers is to extract features from input images. Contribute to hellorp1990/Covid-19-USF development by creating an account on GitHub. While, MPA, BPSO, SCA, and SGA obtained almost the same accuracy, followed by both bGWO, WOA, and SMA. 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. (18)(19) for the second half (predator) as represented below. }\delta (1-\delta )(2-\delta ) U_{i}(t-2)\\&\quad + \frac{1}{4! Google Scholar. }, \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. In this paper, after applying Chi-square, the feature vector is minimized for both datasets from 51200 to 2000. arXiv preprint arXiv:1704.04861 (2017). Ozturk et al. In Proceedings of the IEEE Conference on computer vision and pattern recognition workshops, 806813 (2014). ADS One of the drawbacks of pre-trained models, such as Inception, is that its architecture required large memory requirements as well as storage capacity (92 M.B), which makes deployment exhausting and a tiresome task. J. Clin. Faramarzi et al.37 divided the agents for two halves and formulated Eqs. 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. Tensorflow: Large-scale machine learning on heterogeneous systems, 2015. Efficient classification of white blood cell leukemia with improved swarm optimization of deep features. implemented the FO-MPA swarm optimization and prepared the related figures and manuscript text. Google Scholar. However, some of the extracted features by CNN might not be sufficient, which may affect negatively the quality of the classification images. Havaei, M. et al. Harikumar et al.18 proposed an FS method based on wavelets to classify normality or abnormality of different types of medical images, such as CT, MRI, ultrasound, and mammographic images. COVID-19 image classification using deep features and fractional-order marine predators algorithm Authors. In54, AlexNet pre-trained network was used to extract deep features then applied PCA to select the best features by eliminating highly correlated features. A. what medical images are commonly used for COVID-19 classification and what are the methods for COVID-19 classification. https://doi.org/10.1038/s41598-020-71294-2, DOI: https://doi.org/10.1038/s41598-020-71294-2. 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. 6, right), our approach still provides an overall accuracy of 99.68%, putting it first with a slight advantage over MobileNet (99.67 %). PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer. Also, they require a lot of computational resources (memory & storage) for building & training. IEEE Trans. Image Classification With ResNet50 Convolution Neural Network (CNN) on Covid-19 Radiography | by Emmanuella Anggi | The Startup | Medium 500 Apologies, but something went wrong on our end.. The main purpose of Conv. It achieves a Dice score of 0.9923 for segmentation, and classification accuracies of 0. & Mirjalili, S. Slime mould algorithm: A new method for stochastic optimization. A. 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). In this paper, we used two different datasets. Its structure is designed based on experts' knowledge and real medical process. Kharrat, A. 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 . Two real datasets about COVID-19 patients are studied in this paper. 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. Generally, the most stable algorithms On dataset 1 are WOA, SCA, HGSO, FO-MPA, and SGA, respectively. 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 . Softw. PubMed The algorithm combines the assessment of image quality, digital image processing and deep learning for segmentation of the lung tissues and their classification. 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. Litjens, G. et al. Szegedy, C. et al. 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. Med. . 101, 646667 (2019). The \(\delta\) symbol refers to the derivative order coefficient. 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. This stage can be mathematically implemented as below: In Eq. A.A.E. 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. For example, Lambin et al.7 proposed an efficient approach called Radiomics to extract medical image features. 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. Continuing on my commitment to share small but interesting things in Google Cloud, this time I created a model for a is applied before larger sized kernels are applied to reduce the dimension of the channels, which accordingly, reduces the computation cost. They showed that analyzing image features resulted in more information that improved medical imaging. It also shows that FO-MPA can select the smallest subset of features, which reflects positively on performance. Moreover, the \(R_B\) parameter has been changed to depend on weibull distribution as described below. Syst. Duan et al.13 applied the Gaussian mixture model (GMM) to extract features from pulmonary nodules from CT images. Also, other recent published works39, who combined a CNN architecture with Weighted Symmetric Uncertainty (WSU) to select optimal features for traffic classification. Although convolutional neural networks (CNNs) is considered the current state-of-the-art image classification technique, it needs massive computational cost for deployment and training. The test accuracy obtained for the model was 98%. 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. Finally, the predator follows the levy flight distribution to exploit its prey location. Covid-19 dataset. Civit-Masot et al. However, using medical imaging, chest CT, and chest X-ray scan can play a critical role in COVID-19 diagnosis. & Baby, C.J. Emphysema medical image classification using fuzzy decision tree with fuzzy particle swarm optimization clustering. Transmission scenarios for middle east respiratory syndrome coronavirus (mers-cov) and how to tell them apart. 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. 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. The MCA-based model is used to process decomposed images for further classification with efficient storage. Etymology. The predator tries to catch the prey while the prey exploits the locations of its food. Besides, the binary classification between two classes of COVID-19 and normal chest X-ray is proposed. It also contributes to minimizing resource consumption which consequently, reduces the processing time. Decis. Wu, Y.-H. etal. 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 . However, the proposed FO-MPA approach has an advantage in performance compared to other works. Comput. (20), \(FAD=0.2\), and W is a binary solution (0 or 1) that corresponded to random solutions. 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. Narayanan, S.J., Soundrapandiyan, R., Perumal, B. Li et al.36 proposed an FS method using a discrete artificial bee colony (ABC) to improve the classification of Parkinsons disease. PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer. The evaluation outcomes demonstrate that ABC enhanced precision, and also it reduced the size of the features. We have used RMSprop optimizer for weight updates, cross entropy loss function and selected learning rate as 0.0001. Medical imaging techniques are very important for diagnosing diseases. Support Syst. It is important to detect positive cases early to prevent further spread of the outbreak. 1. 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. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770778 (2016). 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 . (2) To extract various textural features using the GLCM algorithm. (14)(15) to emulate the motion of the first half of the population (prey) and Eqs. ADS Johnson, D.S., Johnson, D. L.L., Elavarasan, P. & Karunanithi, A. volume10, Articlenumber:15364 (2020) 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. 25, 3340 (2015). Based on54, the later step reduces the memory requirements, and improve the efficiency of the framework. The memory terms of the prey are updated at the end of each iteration based on first in first out concept. Huang, P. et al. Math. Sign up for the Nature Briefing newsletter what matters in science, free to your inbox daily. Google Scholar. 121, 103792 (2020). (5). https://doi.org/10.1016/j.future.2020.03.055 (2020). 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). 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. The Marine Predators Algorithm (MPA)is a recently developed meta-heuristic algorithm that emulates the relation among the prey and predator in nature37. Software available from tensorflow. (24). 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). 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 . 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. For example, Da Silva et al.30 used the genetic algorithm (GA) to develop feature selection methods for ranking the quality of medical images. Deep cnns for microscopic image classification by exploiting transfer learning and feature concatenation. 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. 4a, the SMA was considered as the fastest algorithm among all algorithms followed by BPSO, FO-MPA, and HHO, respectively, while MPA was the slowest algorithm. The memory properties of Fc calculus makes it applicable to the fields that required non-locality and memory effect. Dual feature selection and rebalancing strategy using metaheuristic optimization algorithms in x-ray image datasets. They were manually aggregated from various web based repositories into a machine learning (ML) friendly format with accompanying data loader code. Hence, the FC memory is applied during updating the prey locating in the second step of the algorithm to enhance the exploitation stage. https://doi.org/10.1155/2018/3052852 (2018). Computational image analysis techniques play a vital role in disease treatment and diagnosis. Nguyen, L.D., Lin, D., Lin, Z. Med. Furthermore, deep learning using CNN is considered one of the best choices in medical imaging applications20, especially classification. Al-qaness, M. A., Ewees, A. TOKYO, Jan 26 (Reuters) - Japan is set to downgrade its classification of COVID-19 to that of a less serious disease on May 8, revising its measures against the coronavirus such as relaxing. Feature selection using flower pollination optimization to diagnose lung cancer from ct images. 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. Table2 shows some samples from two datasets. FP (false positives) are the positive COVID-19 images that were incorrectly labeled as negative COVID-19, while FN (false negatives) are the negative COVID-19 images that were mislabeled as positive COVID-19 images. Using X-ray images we can train a machine learning classifier to detect COVID-19 using Keras and TensorFlow. Also, image segmentation can extract critical features, including the shape of tissues, and texture5,6. 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. Mirjalili, S. & Lewis, A. In some cases (as exists in this work), the dataset is limited, so it is not sufficient for building & training a CNN. \(\bigotimes\) indicates the process of element-wise multiplications. Rep. 10, 111 (2020). Therefore, several pre-trained models have won many international image classification competitions such as VGGNet24, Resnet25, Nasnet26, Mobilenet27, Inception28 and Xception29. (8) at \(T = 1\), the expression of Eq. 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: Appl. 9, 674 (2020). Sohail, A. S.M., Bhattacharya, P., Mudur, S.P. & Krishnamurthy, S. Classification of ultrasound medical images using distance based feature selection and fuzzy-svm. where \(REfi_{i}\) represents the importance of feature i that were calculated from all trees, where \(normfi_{ij}\) is the normalized feature importance for feature i in tree j, also T is the total number of trees. Some people say that the virus of COVID-19 is. My education and internships have equipped me with strong technical skills in Python, deep learning models, machine learning classification, text classification, and more. 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. Eng. Figure5 illustrates the convergence curves for FO-MPA and other algorithms in both datasets. 2. Sci. . COVID-19 Chest X -Ray Image Classification with Neural Network Currently we are suffering from COVID-19, and the situation is very serious. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Decaf: A deep convolutional activation feature for generic visual recognition. Robustness-driven feature selection in classification of fibrotic interstitial lung disease patterns in computed tomography using 3d texture features. Going deeper with convolutions. <span> <h5>Background</h5> <p>The COVID19 pandemic has precipitated global apprehensions about increased fatalities and raised concerns about gaps in healthcare . SMA is on the second place, While HGSO, SCA, and HHO came in the third to fifth place, respectively. The code of the proposed approach is also available via the following link [https://drive.google.com/file/d/1-oK-eeEgdCMCnykH364IkAK3opmqa9Rvasx/view?usp=sharing]. Appl. Inf. 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. After applying this technique, the feature vector is minimized from 2000 to 459 and from 2000 to 462 for Dataset1 and Dataset 2, respectively. Phys. 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. a cough chills difficulty breathing tiredness body aches headaches a new loss of taste or smell a sore throat nausea and vomiting diarrhea Not everyone with COVID-19 develops all of these. Book Metric learning Metric learning can create a space in which image features within the. Coronavirus Disease (COVID-19): A primer for emergency physicians (2020) Summer Chavez et al. However, it has some limitations that affect its quality. Acharya et al.11 applied different FS methods to classify Alzheimers disease using MRI images. \(Fit_i\) denotes a fitness function value. The authors declare no competing interests. For more analysis of feature selection algorithms based on the number of selected features (S.F) and consuming time, Fig. Cancer 48, 441446 (2012). After feature extraction, we applied FO-MPA to select the most significant features. Ozturk, T. et al. The following stage was to apply Delta variants. Propose similarity regularization for improving C. Rajpurkar, P. etal. 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 . 2022 May;144:105350. doi: 10.1016/j.compbiomed.2022.105350. All classication models ever, the virus mutates, and new variants emerge and dis- performed better in classifying the Non-COVID-19 images appear. Stage 2 has been executed in the second third of the total number of iterations when \(\frac{1}{3}t_{max}< t< \frac{2}{3}t_{max}\). & Carlsson, S. Cnn features off-the-shelf: an astounding baseline for recognition. 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. Chollet, F. Keras, a python deep learning library. The combination of SA and GA showed better performances than the original SA and GA. Narayanan et al.33 proposed a fuzzy particle swarm optimization (PSO) as an FS method to enhance the classification of CT images of emphysema. The different proposed models will be trained with three-class balanced dataset which consists of 3000 images, 1000 images for each class. However, it was clear that VGG19 and MobileNet achieved the best performance over other CNNs. In this paper, we apply a convolutional neural network (CNN) to extract features from COVID-19 X-Ray images. 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. Intell. Automated detection of covid-19 cases using deep neural networks with x-ray images. In Dataset 2, FO-MPA also is reported as the highest classification accuracy with the best and mean measures followed by the BPSO. Biocybern. IEEE Trans. Whereas the worst one was SMA algorithm. In our experiment, we randomly split the data into 70%, 10%, and 20% for training, validation, and testing sets, respectively. The shape of the output from the Inception is (5, 5, 2048), which represents a feature vector of size 51200. 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).. Also, because COVID-19 is a virus, distinguish COVID-19 from common viral . An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm. This dataset currently contains hundreds of frontal view X-rays and is the largest public resource for COVID-19 image and prognostic data, making it a necessary resource to develop and evaluate tools to aid in the treatment of CO VID-19. Moreover, the Weibull distribution employed to modify the exploration function. 95, 5167 (2016). 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.

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