medRxiv (2020). Number of extracted feature and classification accuracy by FO-MPA compared to other CNNs on dataset 1 (left) and on dataset 2 (right). Whereas, the worst algorithm was BPSO. Also, As seen in Fig. The announcement confirmed that from May 8, following Japan's Golden Week holiday period, COVID-19 will be officially downgraded to Class 5, putting the virus on the same classification level as seasonal influenza. Syst. (23), the general formulation for the solutions of FO-MPA based on FC memory perspective can be written as follows: After checking the previous formula, it can be detected that the motion of the prey becomes based on some terms from the previous solutions with a length of (m), as depicted in Fig. is applied before larger sized kernels are applied to reduce the dimension of the channels, which accordingly, reduces the computation cost. Software available from tensorflow. Litjens, G. et al. 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. 69, 4661 (2014). Thereafter, the FO-MPA parameters are applied to update the solutions of the current population. Kong, Y., Deng, Y. 11314, 113142S (International Society for Optics and Photonics, 2020). On the second dataset, dataset 2 (Fig. While55 used different CNN structures. Deep Learning Based Image Classification of Lungs Radiography for Detecting COVID-19 using a Deep CNN and ResNet 50 Also, WOA algorithm showed good results in all measures, unlike dataset 1, which can conclude that no algorithm can solve all kinds of problems. Figure6 shows a comparison between our FO-MPA approach and other CNN architectures. 2 (right). https://doi.org/10.1038/s41598-020-71294-2, DOI: https://doi.org/10.1038/s41598-020-71294-2. Recently, a combination between the fractional calculus tool and the meta-heuristics opens new doors in providing robust and reliable variants41. 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: where \(R\in [0,1]\) is a random vector drawn from a uniform distribution and \(P=0.5\) is a constant number. 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. Google Scholar. The first one is based on Python, where the deep neural network architecture (Inception) was built and the feature extraction part was performed. In this paper, Inception is applied as a feature extractor, where the input image shape is (229, 229, 3). Among the FS methods, the metaheuristic techniques have been established their performance overall other FS methods when applied to classify medical images. Inception architecture is described in Fig. The different proposed models will be trained with three-class balanced dataset which consists of 3000 images, 1000 images for each class. 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. 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. Robertas Damasevicius. 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. However, the proposed IMF approach achieved the best results among the compared algorithms in least time. Narayanan, S.J., Soundrapandiyan, R., Perumal, B. 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 . Mobilenets: Efficient convolutional neural networks for mobile vision applications. (2) To extract various textural features using the GLCM algorithm. Google Scholar. arXiv preprint arXiv:2003.11597 (2020). All classication models ever, the virus mutates, and new variants emerge and dis- performed better in classifying the Non-COVID-19 images appear. D.Y. To obtain However, it was clear that VGG19 and MobileNet achieved the best performance over other CNNs. 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. Harikumar, R. & Vinoth Kumar, B. The Weibull Distribution is a heavy-tied distribution which presented as in Fig. Figure7 shows the most recent published works as in54,55,56,57 and44 on both dataset 1 and dataset 2. 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. ), such as \(5\times 5\), \(3 \times 3\), \(1 \times 1\). Background The large volume and suboptimal image quality of portable chest X-rays (CXRs) as a result of the COVID-19 pandemic could post significant challenges for radiologists and frontline physicians. In14, the authors proposed an FS method based on a convolutional neural network (CNN) to detect pneumonia from lung X-ray images. COVID 19 X-ray image classification. 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. CNNs are more appropriate for large datasets. Future Gener. 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. Pool layers are used mainly to reduce the inputs size, which accelerates the computation as well. In this paper, we used two different datasets. (33)), showed that FO-MPA also achieved the best value of the fitness function compared to others. Nguyen, L.D., Lin, D., Lin, Z. By submitting a comment you agree to abide by our Terms and Community Guidelines. Keywords - Journal. 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). Propose similarity regularization for improving C. Also, image segmentation can extract critical features, including the shape of tissues, and texture5,6. Figure5 illustrates the convergence curves for FO-MPA and other algorithms in both datasets. Contribute to hellorp1990/Covid-19-USF development by creating an account on GitHub. Efficient classification of white blood cell leukemia with improved swarm optimization of deep features. Multimedia Tools Appl. Future Gener. Such methods might play a significant role as a computer-aided tool for image-based clinical diagnosis soon. & Cmert, Z. 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. For the image features that led to categorization, there were varied levels of agreement in the interobserver and intraobserver components that . Afzali, A., Mofrad, F.B. 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. A. The conference was held virtually due to the COVID-19 pandemic. 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. In our example the possible classifications are covid, normal and pneumonia. 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. Bibliographic details on CECT: Controllable Ensemble CNN and Transformer for COVID-19 image classification by capturing both local and global image features. 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). Liao, S. & Chung, A. C. Feature based nonrigid brain mr image registration with symmetric alpha stable filters. The memory terms of the prey are updated at the end of each iteration based on first in first out concept. Chollet, F. Keras, a python deep learning library. Computed tomography (CT) and magnetic resonance imaging (MRI) represent valuable input to AI algorithms, scanning human body sections for the sake of diagnosis. In addition, up to our knowledge, MPA has not applied to any real applications yet. Detection of lung cancer on chest ct images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. He, K., Zhang, X., Ren, S. & Sun, J. In Medical Imaging 2020: Computer-Aided Diagnosis, vol. Int. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 19 (2015). Use of chest ct in combination with negative rt-pcr assay for the 2019 novel coronavirus but high clinical suspicion. Lilang Zheng, Jiaxuan Fang, Xiaorun Tang, Hanzhang Li, Jiaxin Fan, Tianyi Wang, Rui Zhou, Zhaoyan Yan. HGSO was ranked second with 146 and 87 selected features from Dataset 1 and Dataset 2, respectively. Accordingly, that reflects on efficient usage of memory, and less resource consumption. While, MPA, BPSO, SCA, and SGA obtained almost the same accuracy, followed by both bGWO, WOA, and SMA. (1): where \(O_k\) and \(E_k\) refer to the actual and the expected feature value, respectively. The proposed IMF approach successfully achieves two important targets, selecting small feature numbers with high accuracy. 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. IEEE Trans. An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm. Purpose The study aimed at developing an AI . Artif. IRBM https://doi.org/10.1016/j.irbm.2019.10.006 (2019). In9, to classify ultrasound medical images, the authors used distance-based FS methods and a Fuzzy Support Vector Machine (FSVM). 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. 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 predator tries to catch the prey while the prey exploits the locations of its food. chest X-ray images into three classes of COVID-19, normal chest X-ray and other lung diseases. 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. They used K-Nearest Neighbor (kNN) to classify x-ray images collected from Montgomery dataset, and it showed good performances. Zhang, N., Ruan, S., Lebonvallet, S., Liao, Q. 22, 573577 (2014). They shared some parameters, such as the total number of iterations and the number of agents which were set to 20 and 15, respectively. 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 . Both the model uses Lungs CT Scan images to classify the covid-19. contributed to preparing results and the final figures. Wu, Y.-H. etal. 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. arXiv preprint arXiv:2004.05717 (2020). In Eq. In this paper, filters of size 2, besides a stride of 2 and \(2 \times 2\) as Max pool, were adopted. Cauchemez, S. et al. Sci. 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. (8) at \(T = 1\), the expression of Eq. Toaar, M., Ergen, B. Going deeper with convolutions. }\delta (1-\delta ) U_{i}(t-1)+ \frac{1}{3! It is calculated between each feature for all classes, as in Eq. 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. It is also noted that both datasets contain a small number of positive COVID-19 images, and up to our knowledge, there is no other sufficient available published dataset for COVID-19. Feature selection based on gaussian mixture model clustering for the classification of pulmonary nodules based on computed tomography. Howard, A.G. etal. 11, 243258 (2007). Image Anal. 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. Book Comput. implemented the deep neural networks and classification as well as prepared the related figures and manuscript text. We have used RMSprop optimizer for weight updates, cross entropy loss function and selected learning rate as 0.0001. In54, AlexNet pre-trained network was used to extract deep features then applied PCA to select the best features by eliminating highly correlated features. 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. Introduction Adv. Moreover, we design a weighted supervised loss that assigns higher weight for . In this paper, after applying Chi-square, the feature vector is minimized for both datasets from 51200 to 2000. 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. 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. arXiv preprint arXiv:2003.13815 (2020). 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. The algorithm combines the assessment of image quality, digital image processing and deep learning for segmentation of the lung tissues and their classification. One of the main disadvantages of our approach is that its built basically within two different environments. (3), the importance of each feature is then calculated. According to the best measure, the FO-MPA performed similarly to the HHO algorithm, followed by SMA, HGSO, and SCA, respectively. A., Fan, H. & Abd ElAziz, M. Optimization method for forecasting confirmed cases of covid-19 in china. Article The second one is based on Matlab, where the feature selection part (FO-MPA algorithm) was performed. The shape of the output from the Inception is (5, 5, 2048), which represents a feature vector of size 51200. & Mirjalili, S. Slime mould algorithm: A new method for stochastic optimization. kharrat and Mahmoud32proposed an FS method based on a hybrid of Simulated Annealing (SA) and GA to classify brain tumors using MRI. PubMed Li, H. etal. 115, 256269 (2011). & Pouladian, M. Feature selection for contour-based tuberculosis detection from chest x-ray images. The prey follows Weibull distribution during discovering the search space to detect potential locations of its food. Objective: To help improve radiologists' efficacy of disease diagnosis in reading computed tomography (CT) images, this study aims to investigate the feasibility of applying a modified deep learning (DL) method as a new strategy to automatically segment disease-infected regions and predict disease severity. The dataset consists of 21,165 chest X-ray (CXR) COVID-19 images distributed on four categories which are COVID19, lung opacity, viral pneumonia, and NORMAL (Non-COVID). The proposed CNN architecture for Task 2 consists of 14 weighted layers, in which there are three convolutional layers and one fully connected layer, as shown in Fig. They applied the SVM classifier for new MRI images to segment brain tumors, automatically. 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. In 2019 26th National and 4th International Iranian Conference on Biomedical Engineering (ICBME), 194198 (IEEE, 2019). Springer Science and Business Media LLC Online. 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/]. Med. The results show that, using only 6 epochs for training, the CNNs achieved very high performance on the classification task. The following stage was to apply Delta variants. My education and internships have equipped me with strong technical skills in Python, deep learning models, machine learning classification, text classification, and more. In this work, the MPA is enhanced by fractional calculus memory feature, as a result, Fractional-order Marine Predators Algorithm (FO-MPA) is introduced. J. Med. Sci Rep 10, 15364 (2020). While the second half of the agents perform the following equations. Appl. Continuing on my commitment to share small but interesting things in Google Cloud, this time I created a model for a 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. Remainder sections are organized as follows: Material and methods sectionpresents the methodology and the techniques used in this work including model structure and description. In Iberian Conference on Pattern Recognition and Image Analysis, 176183 (Springer, 2011). Adv. & Wang, W. Medical image segmentation using fruit fly optimization and density peaks clustering. Finally, the sum of the features importance value on each tree is calculated then divided by the total number of trees as in Eq. Netw. This paper reviews the recent progress of deep learning in COVID-19 images applications from five aspects; Firstly, 33 COVID-19 datasets and data enhancement methods are introduced; Secondly, COVID-19 classification methods . where CF is the parameter that controls the step size of movement for the predator. 6, right), our approach still provides an overall accuracy of 99.68%, putting it first with a slight advantage over MobileNet (99.67 %). 9, 674 (2020). 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]. https://doi.org/10.1016/j.future.2020.03.055 (2020). 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. It is important to detect positive cases early to prevent further spread of the outbreak. For more analysis of feature selection algorithms based on the number of selected features (S.F) and consuming time, Fig. Appl. Then, applying the FO-MPA to select the relevant features from the images. The variants of concern are Alpha, Beta, Gamma, and than the COVID-19 images. Med. 2022 May;144:105350. doi: 10.1016/j.compbiomed.2022.105350. To survey the hypothesis accuracy of the models. 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. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Math. Syst. Vis. 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. By filtering titles, abstracts, and content in the Google Scholar database, this literature review was able to find 19 related papers to answer two research questions, i.e. & Dai, Q. Discriminative clustering and feature selection for brain mri segmentation. There are three main parameters for pooling, Filter size, Stride, and Max pool. 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. Very deep convolutional networks for large-scale image recognition. A. et al. However, it has some limitations that affect its quality. The next process is to compute the performance of each solution using fitness value and determine which one is the best solution. Johnson, D.S., Johnson, D. L.L., Elavarasan, P. & Karunanithi, A. Comput. Shi, H., Li, H., Zhang, D., Cheng, C. & Cao, X. Harris hawks optimization: algorithm and applications. all above stages are repeated until the termination criteria is satisfied. Scientific Reports (Sci Rep) J. Med. Dual feature selection and rebalancing strategy using metaheuristic optimization algorithms in x-ray image datasets. 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. 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. For example, Lambin et al.7 proposed an efficient approach called Radiomics to extract medical image features. 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. 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. 41, 923 (2019). Automatic CNN-based Chest X-Ray (CXR) image classification for detecting Covid-19 attracted so much attention. Also, some image transformations were applied, such as rotation, horizontal flip, and scaling. Improving the ranking quality of medical image retrieval using a genetic feature selection method. & Cmert, Z. J. In Table4, for Dataset 1, the proposed FO-MPA approach achieved the highest accuracy in the best and mean measures, as it reached 98.7%, and 97.2% of correctly classified samples, respectively. 101, 646667 (2019). Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. 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. Also, they require a lot of computational resources (memory & storage) for building & training. Also, because COVID-19 is a virus, distinguish COVID-19 from common viral . arXiv preprint arXiv:1409.1556 (2014). 35, 1831 (2017). Automated detection of alzheimers disease using brain mri imagesa study with various feature extraction techniques. \(\Gamma (t)\) indicates gamma function. Comput. Our method is able to classify pneumonia from COVID-19 and visualize an abnormal area at the same time. Isolation and characterization of a bat sars-like coronavirus that uses the ace2 receptor. Eng. MathSciNet & Cao, J. 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 . Methods: We employed a public dataset acquired from 20 COVID-19 patients, which . It achieves a Dice score of 0.9923 for segmentation, and classification accuracies of 0. Moreover, other COVID-19 positive images were added by the Italian Society of Medical and Interventional Radiology (SIRM) COVID-19 Database45. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from . Comput. Eq. In general, MPA is a meta-heuristic technique that simulates the behavior of the prey and predator in nature37. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition12511258 (2017). 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. Li, S., Chen, H., Wang, M., Heidari, A. It can be concluded that FS methods have proven their advantages in different medical imaging applications19. 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SMA is on the second place, While HGSO, SCA, and HHO came in the third to fifth place, respectively. Ge, X.-Y. where \(ni_{j}\) is the importance of node j, while \(w_{j}\) refers to the weighted number of samples reaches the node j, also \(C_{j}\) determines the impurity value of node j. left(j) and right(j) are the child nodes from the left split and the right split on node j, respectively. Moreover, the Weibull distribution employed to modify the exploration function. and M.A.A.A. where \(R_L\) has random numbers that follow Lvy distribution. In such a case, in order to get the advantage of the power of CNN and also, transfer learning can be applied to minimize the computational costs21,22.
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