Eng. They are distributed among people, bats, mice, birds, livestock, and other animals1,2. Jcs: An explainable covid-19 diagnosis system by joint classification and segmentation. IEEE Trans. Med. 6 (left), for dataset 1, it can be seen that our proposed FO-MPA approach outperforms other CNN models like VGGNet, Xception, Inception, Mobilenet, Nasnet, and Resnet. Google Scholar. Syst. Feature selection using flower pollination optimization to diagnose lung cancer from ct images. Google Research, https://research.googleblog.com/2017/11/automl-for-large-scaleimage.html, Blog (2017). 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 . 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. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770778 (2016). EMRes-50 model . Li et al.34 proposed a self-adaptive bat algorithm (BA) to address two problems in lung X-ray images, rebalancing, and feature selection. Efficient classification of white blood cell leukemia with improved swarm optimization of deep features. J. Med. Refresh the page, check Medium 's site status, or find something interesting. Average of the consuming time and the number of selected features in both datasets. Epub 2022 Mar 3. FC provides a clear interpretation of the memory and hereditary features of the process. COVID-19 is the most transmissible disease, caused by the SARS-CoV-2 virus that severely infects the lungs and the upper respiratory tract of the human body.This virus badly affected the lives and wellness of millions of people worldwide and spread widely. We can call this Task 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. & Cmert, Z. Cohen, J.P., Morrison, P. & Dao, L. Covid-19 image data collection. Classification of COVID-19 X-ray images with Keras and its potential problem | by Yiwen Lai | Analytics Vidhya | Medium Write Sign up 500 Apologies, but something went wrong on our end.. After feature extraction, we applied FO-MPA to select the most significant features. While no feature selection was applied to select best features or to reduce model complexity. Vis. Design incremental data augmentation strategy for COVID-19 CT data. & Dai, Q. Discriminative clustering and feature selection for brain mri segmentation. Med. 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. Moreover, we design a weighted supervised loss that assigns higher weight for . org (2015). SharifRazavian, A., Azizpour, H., Sullivan, J. It can be concluded that FS methods have proven their advantages in different medical imaging applications19. Adv. Liao, S. & Chung, A. C. Feature based nonrigid brain mr image registration with symmetric alpha stable filters. Table4 show classification accuracy of FO-MPA compared to other feature selection algorithms, where the best, mean, and STD for classification accuracy were calculated for each one, besides time consumption and the number of selected features (SF). Extensive evaluation experiments had been carried out with a collection of two public X-ray images datasets. https://doi.org/10.1155/2018/3052852 (2018). medRxiv (2020). Negative COVID-19 images were collected from another Chest X-ray Kaggle published dataset43. Afzali, A., Mofrad, F.B. Objective: Lung image classification-assisted diagnosis has a large application market. The proposed approach was evaluated on two public COVID-19 X-ray datasets which achieves both high performance and reduction of computational complexity. 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. Comput. 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. Stage 2 has been executed in the second third of the total number of iterations when \(\frac{1}{3}t_{max}< t< \frac{2}{3}t_{max}\). & Wang, W. Medical image segmentation using fruit fly optimization and density peaks clustering. They applied a fuzzy decision tree classifier, and they found that fuzzy PSO improved the classification accuracy. Nguyen, L.D., Lin, D., Lin, Z. 25, 3340 (2015). 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. COVID-19 image classification using deep features and fractional-order marine predators algorithm Authors. Besides, the used statistical operations improve the performance of the FO-MPA algorithm because it supports the algorithm in selecting only the most important and relevant features. SMA is on the second place, While HGSO, SCA, and HHO came in the third to fifth place, respectively. 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. Google Scholar. where \(R\in [0,1]\) is a random vector drawn from a uniform distribution and \(P=0.5\) is a constant number. Also, in12, an Fs method based on SVM was proposed to detect Alzheimers disease from SPECT images. Methods: We employed a public dataset acquired from 20 COVID-19 patients, which . (15) can be reformulated to meet the special case of GL definition of Eq. 51, 810820 (2011). 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. Eur. 2. Multimedia Tools Appl. 79, 18839 (2020). In our experiment, we randomly split the data into 70%, 10%, and 20% for training, validation, and testing sets, respectively. Support Syst. PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer. (5). and JavaScript. For instance,\(1\times 1\) conv. They concluded that the hybrid method outperformed original fuzzy c-means, and it had less sensitive to noises. Wu, Y.-H. etal. The results are the best achieved on these datasets when compared to a set of recent feature selection algorithms. Radiomics: extracting more information from medical images using advanced feature analysis. Johnson, D.S., Johnson, D. L.L., Elavarasan, P. & Karunanithi, A. To segment brain tissues from MRI images, Kong et al.17 proposed an FS method using two methods, called a discriminative clustering method and the information theoretic discriminative segmentation. 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. Software available from tensorflow. It also contributes to minimizing resource consumption which consequently, reduces the processing time. 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 . Li, J. et al. arXiv preprint arXiv:1704.04861 (2017). kharrat and Mahmoud32proposed an FS method based on a hybrid of Simulated Annealing (SA) and GA to classify brain tumors using MRI. Convolutional neural networks were implemented in Python 3 under Google Colaboratory46, commonly referred to as Google Colab, which is a research project for prototyping machine learning models on powerful hardware options such as GPUs and TPUs. 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 . Biocybern. Moreover, the \(R_B\) parameter has been changed to depend on weibull distribution as described below. Can ai help in screening viral and covid-19 pneumonia? The name "pangolin" comes from the Malay word pengguling meaning "one who rolls up" from guling or giling "to roll"; it was used for the Sunda pangolin (Manis javanica). Then the best solutions are reached which determine the optimal/relevant features that should be used to address the desired output via several performance measures. The Shearlet transform FS method showed better performances compared to several FS methods. For this motivation, we utilize the FC concept with the MPA algorithm to boost the second step of the standard version of the algorithm. They compared the BA to PSO, and the comparison outcomes showed that BA had better performance. The predator uses the Weibull distribution to improve the exploration capability. 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. While, MPA, BPSO, SCA, and SGA obtained almost the same accuracy, followed by both bGWO, WOA, and SMA. Furthermore, deep learning using CNN is considered one of the best choices in medical imaging applications20, especially classification. Deep learning plays an important role in COVID-19 images diagnosis. 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. In addition, up to our knowledge, MPA has not applied to any real applications yet. where CF is the parameter that controls the step size of movement for the predator. Thank you for visiting nature.com. https://www.sirm.org/category/senza-categoria/covid-19/ (2020). Eq. Nature 503, 535538 (2013). The whale optimization algorithm. The test accuracy obtained for the model was 98%. Bukhari, S. U.K., Bukhari, S. S.K., Syed, A. (33)), showed that FO-MPA also achieved the best value of the fitness function compared to others. An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm. 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. The symbol \(r\in [0,1]\) represents a random number. Multimedia Tools Appl. IEEE Signal Process. For both datasets, the Covid19 images were collected from patients with ages ranging from 40-84 from both genders. Whereas, FO-MPA, MPA, HGSO, and WOA showed similar STD results. Duan, H. et al. Also, other recent published works39, who combined a CNN architecture with Weighted Symmetric Uncertainty (WSU) to select optimal features for traffic classification. A. et al. 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. used a dark Covid-19 network for multiple classification experiments on Covid-19 with an accuracy of 87% [ 23 ]. 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. In this subsection, the performance of the proposed COVID-19 classification approach is compared to other CNN architectures. Chong, D. Y. et al. The different proposed models will be trained with three-class balanced dataset which consists of 3000 images, 1000 images for each class. Initialize solutions for the prey and predator. Eng. How- individual class performance. However, some of the extracted features by CNN might not be sufficient, which may affect negatively the quality of the classification images. 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. 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. Springer Science and Business Media LLC Online. where r is the run numbers. 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. The model was developed using Keras library47 with Tensorflow backend48. He, K., Zhang, X., Ren, S. & Sun, J. Google Scholar. They also used the SVM to classify lung CT images. Johnson et al.31 applied the flower pollination algorithm (FPA) to select features from CT images of the lung, to detect lung cancers. The experimental results and comparisons with other works are presented inResults and discussion section, while they are discussed in Discussion section Finally, the conclusion is described in Conclusion section. The first one, dataset 1 was collected by Joseph Paul Cohen and Paul Morrison and Lan Dao42, where some COVID-19 images were collected by an Italian Cardiothoracic radiologist. and M.A.A.A. 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. 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. Finally, the predator follows the levy flight distribution to exploit its prey location. Both datasets shared some characteristics regarding the collecting sources. J. Clin. J. wrote the intro, related works and prepare results. First: prey motion based on FC the motion of the prey of Eq. 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. Adv. 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/ With accounting the first four previous events (\(m=4\)) from the memory data with derivative order \(\delta\), the position of prey can be modified as follow; Second: Adjusting \(R_B\) random parameter based on weibull distribution. Abbas, A., Abdelsamea, M.M. & Gaber, M.M. Classification of covid-19 in chest x-ray images using detrac deep convolutional neural network. 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. Health Inf. Brain tumor segmentation with deep neural networks. 2 (right). 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. }, \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. The lowest accuracy was obtained by HGSO in both measures. However, it has some limitations that affect its quality. where \(R_L\) has random numbers that follow Lvy distribution. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Figure6 shows a comparison between our FO-MPA approach and other CNN architectures. where \(fi_{i}\) represents the importance of feature I, while \(ni_{j}\) refers to the importance of node j. In57, ResNet-50 CNN has been applied after applying horizontal flipping, random rotation, random zooming, random lighting, and random wrapping on raw images. My education and internships have equipped me with strong technical skills in Python, deep learning models, machine learning classification, text classification, and more. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition12511258 (2017). 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. A.A.E. In this paper, after applying Chi-square, the feature vector is minimized for both datasets from 51200 to 2000. The MCA-based model is used to process decomposed images for further classification with efficient storage. 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. 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. These datasets contain hundreds of frontal view X-rays and considered the largest public resource for COVID-19 image data. It is important to detect positive cases early to prevent further spread of the outbreak. (3), the importance of each feature is then calculated. However, WOA showed the worst performances in these measures; which means that if it is run in the same conditions several times, the same results will be obtained. In ancient India, according to Aelian, it was . Keywords - Journal. Comparison with other previous works using accuracy measure. }\delta (1-\delta )(2-\delta )(3-\delta ) U_{i}(t-3) + P.R\bigotimes S_i. PubMed Artif. This study presents an investigation on 16 pretrained CNNs for classification of COVID-19 using a large public database of CT scans collected from COVID-19 patients and non-COVID-19 subjects. 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/]. In Proceedings of the IEEE Conference on computer vision and pattern recognition workshops, 806813 (2014). 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). Improving the ranking quality of medical image retrieval using a genetic feature selection method. A. the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Imag. 41, 923 (2019). The combination of Conv. 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. Al-qaness, M. A., Ewees, A. To obtain Covid-19 dataset. The accuracy measure is used in the classification phase. Li, S., Chen, H., Wang, M., Heidari, A. Our proposed approach is called Inception Fractional-order Marine Predators Algorithm (IFM), where we combine Inception (I) with Fractional-order Marine Predators Algorithm (FO-MPA). Softw. More so, a combination of partial differential equations and deep learning was applied for medical image classification by10. The updating operation repeated until reaching the stop condition. Stage 3: This stage executed on the last third of the iteration numbers (\(t>\frac{2}{3}t_{max}\)) where based on the following formula: Eddy formation and Fish Aggregating Devices effect: Faramarzi et al.37 considered the external impacts from the environment, such as the eddy formation or Fish Aggregating Devices (FADs) effects to avoid the local optimum solutions. 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. 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.. Authors For the special case of \(\delta = 1\), the definition of Eq.
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