and JavaScript. Acharya et al.11 applied different FS methods to classify Alzheimers disease using MRI images. In this work, the MPA is enhanced by fractional calculus memory feature, as a result, Fractional-order Marine Predators Algorithm (FO-MPA) is introduced. chest X-ray images into three classes of COVID-19, normal chest X-ray and other lung diseases. Dhanachandra and Chanu35 proposed a hybrid method of dynamic PSO and fuzzy c-means to segment two types of medical images, MRI and synthetic images. CAS The results showed that the proposed approach showed better performances in both classification accuracy and the number of extracted features that positively affect resource consumption and storage efficiency. 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. 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. Table3 shows the numerical results of the feature selection phase for both datasets. Stage 1: After the initialization, the exploration phase is implemented to discover the search space. The GL in the discrete-time form can be modeled as below: where T is the sampling period, and m is the length of the memory terms (memory window). Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. Imag. They are distributed among people, bats, mice, birds, livestock, and other animals1,2. Objective: Lung image classification-assisted diagnosis has a large application market. Four measures for the proposed method and the compared algorithms are listed. Covid-19 Classification Using Deep Learning in Chest X-Ray Images Abstract: Covid-19 virus, which has emerged in the Republic of China in an undetermined cause, has affected the whole world quickly. Biocybern. where \(R_L\) has random numbers that follow Lvy distribution. Eur. Then, using an enhanced version of Marine Predators Algorithm to select only relevant features. Zhang, N., Ruan, S., Lebonvallet, S., Liao, Q. 152, 113377 (2020). In the meantime, to ensure continued support, we are displaying the site without styles 115, 256269 (2011). ISSN 2045-2322 (online). While55 used different CNN structures. 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/]. The family of coronaviruses is considered serious pathogens for people because they infect respiratory, hepatic, gastrointestinal, and neurologic diseases. Liao, S. & Chung, A. C. Feature based nonrigid brain mr image registration with symmetric alpha stable filters. There are three main parameters for pooling, Filter size, Stride, and Max pool. 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. 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. It also shows that FO-MPA can select the smallest subset of features, which reflects positively on performance. 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). In54, AlexNet pre-trained network was used to extract deep features then applied PCA to select the best features by eliminating highly correlated features. 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. Automatic COVID-19 lung images classification system based on convolution neural network. In Future of Information and Communication Conference, 604620 (Springer, 2020). 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. They used different images of lung nodules and breast to evaluate their FS methods. Slider with three articles shown per slide. Continuing on my commitment to share small but interesting things in Google Cloud, this time I created a model for a For general case based on the FC definition, the Eq. They compared the BA to PSO, and the comparison outcomes showed that BA had better performance. Our method is able to classify pneumonia from COVID-19 and visualize an abnormal area at the same time. PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer. A properly trained CNN requires a lot of data and CPU/GPU time. The proposed approach selected successfully 130 and 86 out of 51 K features extracted by inception from dataset 1 and dataset 2, while improving classification accuracy at the same time. Ozturk, T. et al. In general, MPA is a meta-heuristic technique that simulates the behavior of the prey and predator in nature37. Decaf: A deep convolutional activation feature for generic visual recognition. The proposed COVID-19 X-ray classification approach starts by applying a CNN (especially, a powerful architecture called Inception which pre-trained on Imagnet dataset) to extract the discriminant features from raw images (with no pre-processing or segmentation) from the dataset that contains positive and negative COVID-19 images. Sohail, A. S.M., Bhattacharya, P., Mudur, S.P. & Krishnamurthy, S. Classification of ultrasound medical images using distance based feature selection and fuzzy-svm. (2) calculated two child nodes. Donahue, J. et al. Whereas the worst one was SMA algorithm. 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. The survey asked participants to broadly classify the findings of each chest CT into one of the four RSNA COVID-19 imaging categories, then select which imaging features led to their categorization. medRxiv (2020). Sci. For more analysis of feature selection algorithms based on the number of selected features (S.F) and consuming time, Fig. SharifRazavian, A., Azizpour, H., Sullivan, J. The results are the best achieved compared to other CNN architectures and all published works in the same datasets. arXiv preprint arXiv:2004.07054 (2020). Computational image analysis techniques play a vital role in disease treatment and diagnosis. layers is to extract features from input images. In this paper, we used two different datasets. The Weibull Distribution is a heavy-tied distribution which presented as in Fig. Chong, D. Y. et al. Meanwhile, the prey moves effectively based on its memory for the previous events to catch its food, as presented in Eq. In 2018 IEEE International Symposium on Circuits and Systems (ISCAS), 15 (IEEE, 2018). The symbol \(r\in [0,1]\) represents a random number. 25, 3340 (2015). PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer. Remainder sections are organized as follows: Material and methods sectionpresents the methodology and the techniques used in this work including model structure and description. I. S. of Medical Radiology. ), such as \(5\times 5\), \(3 \times 3\), \(1 \times 1\). In Inception, there are different sizes scales convolutions (conv. }\delta (1-\delta )(2-\delta )(3-\delta ) U_{i}(t-3) + P.R\bigotimes S_i. Evaluation outcomes showed that GA based FS methods outperformed traditional approaches, such as filter based FS and traditional wrapper methods. Alhamdulillah, glad to share that our paper entitled "Multi-class classification of brain tumor types from MR Images using EfficientNets" has been accepted for Thank you for visiting nature.com. 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. Stage 2: The prey/predator in this stage begin exploiting the best location that detects for their foods. As a result, the obtained outcomes outperformed previous works in terms of the models general performance measure. \(\bigotimes\) indicates the process of element-wise multiplications. Syst. We can call this Task 2. (15) can be reformulated to meet the special case of GL definition of Eq. 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. An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm. After feature extraction, we applied FO-MPA to select the most significant features. It can be concluded that FS methods have proven their advantages in different medical imaging applications19. 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. Eurosurveillance 18, 20503 (2013). Google Scholar. Nature 503, 535538 (2013). 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). 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. and pool layers, three fully connected layers, the last one performs classification. Whereas, the worst algorithm was BPSO. The Marine Predators Algorithm (MPA)is a recently developed meta-heuristic algorithm that emulates the relation among the prey and predator in nature37. However, it was clear that VGG19 and MobileNet achieved the best performance over other CNNs. 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. Duan, H. et al. Bibliographic details on CECT: Controllable Ensemble CNN and Transformer for COVID-19 image classification by capturing both local and global image features. In addition, the good results achieved by the FO-MPA against other algorithms can be seen as an advantage of FO-MPA, where a balancing between exploration and exploitation stages and escaping from local optima were achieved. Google Scholar. Use of chest ct in combination with negative rt-pcr assay for the 2019 novel coronavirus but high clinical suspicion. Scientific Reports (Sci Rep) Dual feature selection and rebalancing strategy using metaheuristic optimization algorithms in x-ray image datasets. 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. Luz, E., Silva, P.L., Silva, R. & Moreira, G. Towards an efficient deep learning model for covid-19 patterns detection in x-ray images. The main purpose of Conv. COVID-19 (coronavirus disease 2019) is a new viral infection disease that is widely spread worldwide. Based on Standard Deviation measure (STD), the most stable algorithms were SCA, SGA, BPSO, and bGWO, respectively. On January 20, 2023, Japanese Prime Minister Fumio Kishida announced that the country would be downgrading the COVID-19 classification. Softw. All authors discussed the results and wrote the manuscript together. 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. Kong, Y., Deng, Y. Google Scholar. Hashim, F. A., Houssein, E. H., Mabrouk, M. S., Al-Atabany, W. & Mirjalili, S. Henry gas solubility optimization: a novel physics-based algorithm. The memory terms of the prey are updated at the end of each iteration based on first in first out concept. Google Scholar. The whole dataset contains around 200 COVID-19 positive images and 1675 negative COVID19 images. The predator tries to catch the prey while the prey exploits the locations of its food. The evaluation confirmed that FPA based FS enhanced classification accuracy. Moreover, other COVID-19 positive images were added by the Italian Society of Medical and Interventional Radiology (SIRM) COVID-19 Database45. Zhu, H., He, H., Xu, J., Fang, Q. Toaar, M., Ergen, B. Figure3 illustrates the structure of the proposed IMF approach. Test the proposed Inception Fractional-order Marine Predators Algorithm (IFM) approach on two publicity available datasets contain a number of positive negative chest X-ray scan images of COVID-19. 121, 103792 (2020). 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 following stage was to apply Delta variants. Zhang et al.16 proposed a kernel feature selection method to segment brain tumors from MRI images. While the second half of the agents perform the following equations. They applied the SVM classifier with and without RDFS. One of these datasets has both clinical and image data. As seen in Fig. Besides, the binary classification between two classes of COVID-19 and normal chest X-ray is proposed. Sahlol, A.T., Yousri, D., Ewees, A.A. et al. Medical imaging techniques are very important for diagnosing diseases. We have used RMSprop optimizer for weight updates, cross entropy loss function and selected learning rate as 0.0001. 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). Its structure is designed based on experts' knowledge and real medical process. 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. Faramarzi, A., Heidarinejad, M., Mirjalili, S. & Gandomi, A. H. Marine predators algorithm: a nature-inspired metaheuristic. 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. Automated detection of alzheimers disease using brain mri imagesa study with various feature extraction techniques. \(r_1\) and \(r_2\) are the random index of the prey. Article MATH All classication models ever, the virus mutates, and new variants emerge and dis- performed better in classifying the Non-COVID-19 images appear. (14)(15) to emulate the motion of the first half of the population (prey) and Eqs. Al-qaness, M. A., Ewees, A. A. Figure5, shows that FO-MPA shows an efficient and faster convergence than the other optimization algorithms on both datasets. 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. Med. 95, 5167 (2016). Eng. Both datasets shared some characteristics regarding the collecting sources. Article Shi, H., Li, H., Zhang, D., Cheng, C. & Cao, X. 111, 300323. Mirjalili, S. & Lewis, A. (14)-(15) are implemented in the first half of the agents that represent the exploitation. Ozturk et al. Internet Explorer). Technol. Syst. Appl. 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. 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. 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 . It noted that all produced feature vectors by CNNs used in this paper are at least bigger by more than 300 times compared to that produced by FO-MPA in terms of the size of the featureset. is applied before larger sized kernels are applied to reduce the dimension of the channels, which accordingly, reduces the computation cost. 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. Sci Rep 10, 15364 (2020). Refresh the page, check Medium 's site status, or find something interesting. It achieves a Dice score of 0.9923 for segmentation, and classification accuracies of 0. Also, in12, an Fs method based on SVM was proposed to detect Alzheimers disease from SPECT images. In the current work, the values of k, and \(\zeta\) are set to 2, and 2, respectively. Rep. 10, 111 (2020). The evaluation showed that the RDFS improved SVM robustness against reconstruction kernel and slice thickness. 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.. Apostolopoulos, I. D. & Mpesiana, T. A. Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Harris hawks optimization: algorithm and applications. Intell. However, the proposed FO-MPA approach has an advantage in performance compared to other works. We do not present a usable clinical tool for COVID-19 diagnosis, but offer a new, efficient approach to optimize deep learning-based architectures for medical image classification purposes. 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. Podlubny, I. 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 . Very deep convolutional networks for large-scale image recognition. 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). The test accuracy obtained for the model was 98%. A. et al. Bisong, E. Building Machine Learning and Deep Learning Models on Google Cloud Platform (Springer, Berlin, 2019). The shape of the output from the Inception is (5, 5, 2048), which represents a feature vector of size 51200. 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. Phys. In this paper, Inception is applied as a feature extractor, where the input image shape is (229, 229, 3). Corona Virus lung infected X-Ray Images accessible by Kaggle a complete 590 images, which classified in 2 classes: typical and Covid-19. 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. The results of max measure (as in Eq. Also, because COVID-19 is a virus, distinguish COVID-19 from common viral . used VGG16 to classify Covid-19 and achieved good results with an accuracy of 86% [ 22 ]. 51, 810820 (2011). 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. 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 . Thereafter, the FO-MPA parameters are applied to update the solutions of the current population. 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. D.Y. The definitions of these measures are as follows: where TP (true positives) refers to the positive COVID-19 images that were correctly labeled by the classifier, while TN (true negatives) is the negative COVID-19 images that were correctly labeled by the classifier. Image Anal. Imaging 35, 144157 (2015). I am passionate about leveraging the power of data to solve real-world problems. To survey the hypothesis accuracy of the models. The evaluation outcomes demonstrate that ABC enhanced precision, and also it reduced the size of the features. 69, 4661 (2014). 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. In Eq. The model was developed using Keras library47 with Tensorflow backend48. Health Inf. Image Underst. Feature selection using flower pollination optimization to diagnose lung cancer from ct images. Imaging 29, 106119 (2009). 22, 573577 (2014). If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from . So, for a \(4 \times 4\) matrix, will result in \(2 \times 2\) matrix after applying max pooling. Google Scholar. Narayanan, S.J., Soundrapandiyan, R., Perumal, B. 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. The data was collected mainly from retrospective cohorts of pediatric patients from Guangzhou Women and Childrens medical center. where r is the run numbers. For example, Da Silva et al.30 used the genetic algorithm (GA) to develop feature selection methods for ranking the quality of medical images. They showed that analyzing image features resulted in more information that improved medical imaging. 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). 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. Appl. To evaluate the performance of the proposed model, we computed the average of both best values and the worst values (Max) as well as STD and computational time for selecting features. Chollet, F. Xception: Deep learning with depthwise separable convolutions. Regarding the consuming time as in Fig. Coronavirus Disease (COVID-19): A primer for emergency physicians (2020) Summer Chavez et al. Initialize solutions for the prey and predator. & Baby, C.J. Emphysema medical image classification using fuzzy decision tree with fuzzy particle swarm optimization clustering. Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In Proceedings of the IEEE Conference on computer vision and pattern recognition workshops, 806813 (2014). From Fig. \(Fit_i\) denotes a fitness function value. Whereas, FO-MPA, MPA, HGSO, and WOA showed similar STD results. By submitting a comment you agree to abide by our Terms and Community Guidelines. arXiv preprint arXiv:2004.05717 (2020). 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. Such methods might play a significant role as a computer-aided tool for image-based clinical diagnosis soon. To address this challenge, this paper proposes a two-path semi- supervised deep learning model, ssResNet, based on Residual Neural Network (ResNet) for COVID-19 image classification, where two paths refer to a supervised path and an unsupervised path, respectively. In Dataset 2, FO-MPA also is reported as the highest classification accuracy with the best and mean measures followed by the BPSO. contributed to preparing results and the final figures. An efficient feature generation approach based on deep learning and feature selection techniques for traffic classification. In this paper, each feature selection algorithm were exposed to select the produced feature vector from Inception aiming at selecting only the most relevant features. 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. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. The classification accuracy of MPA, WOA, SCA, and SGA are almost the same. 2. 43, 635 (2020). used a dark Covid-19 network for multiple classification experiments on Covid-19 with an accuracy of 87% [ 23 ]. Dhanachandra, N. & Chanu, Y. J. Inspired by this concept, Faramarzi et al.37 developed the MPA algorithm by considering both of a predator a prey as solutions. The optimum path forest (OPF) classifier was applied to classify pulmonary nodules based on CT images. The proposed IFM approach is summarized as follows: Extracting deep features from Inception, where about 51 K features were extracted. For this motivation, we utilize the FC concept with the MPA algorithm to boost the second step of the standard version of the algorithm. \delta U_{i}(t)+ \frac{1}{2! 92, 103662. https://doi.org/10.1016/j.engappai.2020.103662 (2020). Authors They employed partial differential equations for extracting texture features of medical images. Automated detection of covid-19 cases using deep neural networks with x-ray images. This task is achieved by FO-MPA which randomly generates a set of solutions, each of them represents a subset of potential features. 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 . How- individual class performance. Etymology. 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. (22) can be written as follows: By using the discrete form of GL definition of Eq. FC provides a clear interpretation of the memory and hereditary features of the process. Also, other recent published works39, who combined a CNN architecture with Weighted Symmetric Uncertainty (WSU) to select optimal features for traffic classification. You have a passion for computer science and you are driven to make a difference in the research community?
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