Imag. (18)(19) for the second half (predator) as represented below. Therefore, a feature selection technique can be applied to perform this task by removing those irrelevant features. 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. Hashim, F. A., Houssein, E. H., Mabrouk, M. S., Al-Atabany, W. & Mirjalili, S. Henry gas solubility optimization: a novel physics-based algorithm. Google Scholar. 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). Ozturk et al. 42, 6088 (2017). They applied the SVM classifier with and without RDFS. Use the Previous and Next buttons to navigate the slides or the slide controller buttons at the end to navigate through each slide. The second CNN architecture classifies the X-ray image into three classes, i.e., normal, pneumonia, and COVID-19. Initialize solutions for the prey and predator. 51, 810820 (2011). 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. SMA is on the second place, While HGSO, SCA, and HHO came in the third to fifth place, respectively. 6, right), our approach still provides an overall accuracy of 99.68%, putting it first with a slight advantage over MobileNet (99.67 %). 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). The evaluation showed that the RDFS improved SVM robustness against reconstruction kernel and slice thickness. 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. HGSO was ranked second with 146 and 87 selected features from Dataset 1 and Dataset 2, respectively. You are using a browser version with limited support for CSS. Dual feature selection and rebalancing strategy using metaheuristic optimization algorithms in x-ray image datasets. (15) can be reformulated to meet the special case of GL definition of Eq. Knowl. Google Scholar. However, the proposed FO-MPA approach has an advantage in performance compared to other works. Propose a novel robust optimizer called Fractional-order Marine Predators Algorithm (FO-MPA) to select efficiently the huge feature vector produced from the CNN. FC provides a clear interpretation of the memory and hereditary features of the process. . (3), the importance of each feature is then calculated. used a dark Covid-19 network for multiple classification experiments on Covid-19 with an accuracy of 87% [ 23 ]. (iii) To implement machine learning classifiers for classification of COVID and non-COVID image classes. COVID-19 image classification using deep features and fractional-order marine predators algorithm. Comput. Image Anal. Softw. 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. Inceptions layer details and layer parameters of are given in Table1. 2 (right). 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. 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. Figure3 illustrates the structure of the proposed IMF approach. A.A.E. 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. 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. 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. They are distributed among people, bats, mice, birds, livestock, and other animals1,2. & Baby, C.J. Emphysema medical image classification using fuzzy decision tree with fuzzy particle swarm optimization clustering. Med. Finally, the predator follows the levy flight distribution to exploit its prey location. Simonyan, K. & Zisserman, A. Eng. In this subsection, a comparison with relevant works is discussed. Coronavirus disease (Covid-19) is an infectious disease that attacks the respiratory area caused by the severe acute . J. Design incremental data augmentation strategy for COVID-19 CT data. The evaluation outcomes demonstrate that ABC enhanced precision, and also it reduced the size of the features. Med. 7, most works are pre-prints for two main reasons; COVID-19 is the most recent and trend topic; also, there are no sufficient datasets that can be used for reliable results. For diagnosing COVID-19, the RT-PCR (real-time polymerase chain reaction) is a standard diagnostic test, but, it can be considered as a time-consuming test, more so, it also suffers from false negative diagnosing4. Johnson et al.31 applied the flower pollination algorithm (FPA) to select features from CT images of the lung, to detect lung cancers. et al. \(Fit_i\) denotes a fitness function value. 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). Brain tumor segmentation with deep neural networks. }\delta (1-\delta ) U_{i}(t-1)+ \frac{1}{3! 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. https://doi.org/10.1038/s41598-020-71294-2, DOI: https://doi.org/10.1038/s41598-020-71294-2. is applied before larger sized kernels are applied to reduce the dimension of the channels, which accordingly, reduces the computation cost. (1): where \(O_k\) and \(E_k\) refer to the actual and the expected feature value, respectively. New Images of Novel Coronavirus SARS-CoV-2 Now Available NIAID Now | February 13, 2020 This scanning electron microscope image shows SARS-CoV-2 (yellow)also known as 2019-nCoV, the virus that causes COVID-19isolated from a patient in the U.S., emerging from the surface of cells (blue/pink) cultured in the lab. 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. 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. \end{aligned} \end{aligned}$$, $$\begin{aligned} \begin{aligned} U_{i}(t+1)&= \frac{1}{1!} 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. Although the performance of the MPA and bGWO was slightly similar, the performance of SGA and WOA were the worst in both max and min measures. (22) can be written as follows: By using the discrete form of GL definition of Eq. 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. Adv. 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. Article There are three main parameters for pooling, Filter size, Stride, and Max pool. J. Clin. It also shows that FO-MPA can select the smallest subset of features, which reflects positively on performance. Isolation and characterization of a bat sars-like coronavirus that uses the ace2 receptor. The results are the best achieved on these datasets when compared to a set of recent feature selection algorithms. Imaging 29, 106119 (2009). Moreover, the \(R_B\) parameter has been changed to depend on weibull distribution as described below. Li, H. etal. The accuracy measure is used in the classification phase. 43, 635 (2020). Moreover, from Table4, it can be seen that the proposed FO-MPA provides better results in terms of F-Score, as it has the highest value in datatset1 and datatset2 which are 0.9821 and 0.99079, respectively. This study aims to improve the COVID-19 X-ray image classification using feature selection technique by the regression mutual information deep convolution neuron networks (RMI Deep-CNNs). For fair comparison, each algorithms was performed (run) 25 times to produce statistically stable results.The results are listed in Tables3 and4. arXiv preprint arXiv:1704.04861 (2017). In Medical Imaging 2020: Computer-Aided Diagnosis, vol. Cohen, J.P., Morrison, P. & Dao, L. Covid-19 image data collection. The variants of concern are Alpha, Beta, Gamma, and than the COVID-19 images. 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: By submitting a comment you agree to abide by our Terms and Community Guidelines. Google Scholar. Med. The results indicate that all CNN-based architectures outperform the ViT-based architecture in the binary classification of COVID-19 using CT images. 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. Eq. For example, Lambin et al.7 proposed an efficient approach called Radiomics to extract medical image features. Robustness-driven feature selection in classification of fibrotic interstitial lung disease patterns in computed tomography using 3d texture features. Duan, H. et al. Appl. To further analyze the proposed algorithm, we evaluate the selected features by FO-MPA by performing classification. Robertas Damasevicius. Based on Standard Deviation measure (STD), the most stable algorithms were SCA, SGA, BPSO, and bGWO, respectively. 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. I. S. of Medical Radiology. 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. In this work, we have used four transfer learning models, VGG16, InceptionV3, ResNet50, and DenseNet121 for the classification tasks. 2. A. They also used the SVM to classify lung CT images. https://doi.org/10.1016/j.future.2020.03.055 (2020). Table3 shows the numerical results of the feature selection phase for both datasets. 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). Arijit Dey, Soham Chattopadhyay, Ram Sarkar, Dandi Yang, Cristhian Martinez, Jesus Carretero, Jess Alejandro Alzate-Grisales, Alejandro Mora-Rubio, Reinel Tabares-Soto, Lo Dumortier, Florent Gupin, Thomas Grenier, Linda Wang, Zhong Qiu Lin & Alexander Wong, Afnan Al-ali, Omar Elharrouss, Somaya Al-Maaddeed, Robbie Sadre, Baskaran Sundaram, Daniela Ushizima, Zahid Ullah, Muhammad Usman, Jeonghwan Gwak, Scientific Reports Among the FS methods, the metaheuristic techniques have been established their performance overall other FS methods when applied to classify medical images. Stage 2 has been executed in the second third of the total number of iterations when \(\frac{1}{3}t_{max}< t< \frac{2}{3}t_{max}\). Thereafter, the FO-MPA parameters are applied to update the solutions of the current population. 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 . Inf. Mirjalili, S., Mirjalili, S. M. & Lewis, A. Grey wolf optimizer. Heidari, A. Stage 1: After the initialization, the exploration phase is implemented to discover the search space. (5). 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). However, it has some limitations that affect its quality. Comput. Covid-19 dataset. According to the formula10, the initial locations of the prey and predator can be defined as below: where the Elite matrix refers to the fittest predators. arXiv preprint arXiv:2004.07054 (2020). Also, in12, an Fs method based on SVM was proposed to detect Alzheimers disease from SPECT images. The two datasets consist of X-ray COVID-19 images by international Cardiothoracic radiologist, researchers and others published on Kaggle. The Marine Predators Algorithm (MPA)is a recently developed meta-heuristic algorithm that emulates the relation among the prey and predator in nature37. The second one is based on Matlab, where the feature selection part (FO-MPA algorithm) was performed. Syst. 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. After applying this technique, the feature vector is minimized from 2000 to 459 and from 2000 to 462 for Dataset1 and Dataset 2, respectively. Also, other recent published works39, who combined a CNN architecture with Weighted Symmetric Uncertainty (WSU) to select optimal features for traffic classification. \(\bigotimes\) indicates the process of element-wise multiplications. Also, it has killed more than 376,000 (up to 2 June 2020) [Coronavirus disease (COVID-2019) situation reports: (https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/)]. Pool layers are used mainly to reduce the inputs size, which accelerates the computation as well. Sohail, A. S.M., Bhattacharya, P., Mudur, S.P. & Krishnamurthy, S. Classification of ultrasound medical images using distance based feature selection and fuzzy-svm. & Pouladian, M. Feature selection for contour-based tuberculosis detection from chest x-ray images. Med. Appl. Dhanachandra, N. & Chanu, Y. J. 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. (33)), showed that FO-MPA also achieved the best value of the fitness function compared to others. MathSciNet where r is the run numbers. In addition, up to our knowledge, MPA has not applied to any real applications yet. PubMed Central Then, using an enhanced version of Marine Predators Algorithm to select only relevant features. Both datasets shared some characteristics regarding the collecting sources. Using X-ray images we can train a machine learning classifier to detect COVID-19 using Keras and TensorFlow. In this paper, we used TPUs for powerful computation, which is more appropriate for CNN. . 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. Introduction Rep. 10, 111 (2020). They applied a fuzzy decision tree classifier, and they found that fuzzy PSO improved the classification accuracy. They showed that analyzing image features resulted in more information that improved medical imaging. & Cao, J. and JavaScript. Automated detection of covid-19 cases using deep neural networks with x-ray images. Memory FC prospective concept (left) and weibull distribution (right). Imaging 35, 144157 (2015). It is obvious that such a combination between deep features and a feature selection algorithm can be efficient in several image classification tasks. Contribute to hellorp1990/Covid-19-USF development by creating an account on GitHub. They were manually aggregated from various web based repositories into a machine learning (ML) friendly format with accompanying data loader code. wrote the intro, related works and prepare results. We are hiring! Deep residual learning for image recognition. As seen in Fig. The proposed IFM approach is summarized as follows: Extracting deep features from Inception, where about 51 K features were extracted. (14)(15) to emulate the motion of the first half of the population (prey) and Eqs. 97, 849872 (2019). COVID-19 Chest X -Ray Image Classification with Neural Network Currently we are suffering from COVID-19, and the situation is very serious. Also, image segmentation can extract critical features, including the shape of tissues, and texture5,6. In 2019 26th National and 4th International Iranian Conference on Biomedical Engineering (ICBME), 194198 (IEEE, 2019). For example, Da Silva et al.30 used the genetic algorithm (GA) to develop feature selection methods for ranking the quality of medical images. Scientific Reports Volume 10, Issue 1, Pages - Publisher. However, it was clear that VGG19 and MobileNet achieved the best performance over other CNNs. An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm. The memory properties of Fc calculus makes it applicable to the fields that required non-locality and memory effect. In Inception, there are different sizes scales convolutions (conv. Software available from tensorflow. In some cases (as exists in this work), the dataset is limited, so it is not sufficient for building & training a CNN. https://doi.org/10.1155/2018/3052852 (2018). Blog, G. Automl for large scale image classification and object detection. Narayanan, S.J., Soundrapandiyan, R., Perumal, B. In Future of Information and Communication Conference, 604620 (Springer, 2020). The HGSO also was ranked last. Decaf: A deep convolutional activation feature for generic visual recognition. 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. The shape of the output from the Inception is (5, 5, 2048), which represents a feature vector of size 51200. Article Therefore in MPA, for the first third of the total iterations, i.e., \(\frac{1}{3}t_{max}\)). Whereas, the slowest and the insufficient convergences were reported by both SGA and WOA in Dataset 1 and by SGA in Dataset 2. PubMed In Iberian Conference on Pattern Recognition and Image Analysis, 176183 (Springer, 2011). Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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/ Google Scholar. Can ai help in screening viral and covid-19 pneumonia? Toaar, M., Ergen, B. CNNs are more appropriate for large datasets. 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. Inf. As a result, the obtained outcomes outperformed previous works in terms of the models general performance measure. Howard, A.G. etal. In general, MPA is a meta-heuristic technique that simulates the behavior of the prey and predator in nature37. the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in 0.9875 and 0.9961 under binary and multi class classifications respectively. They shared some parameters, such as the total number of iterations and the number of agents which were set to 20 and 15, respectively. Moreover, other COVID-19 positive images were added by the Italian Society of Medical and Interventional Radiology (SIRM) COVID-19 Database45. The whole dataset contains around 200 COVID-19 positive images and 1675 negative COVID19 images. Authors For both datasets, the Covid19 images were collected from patients with ages ranging from 40-84 from both genders. A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models. https://www.sirm.org/category/senza-categoria/covid-19/ (2020). A survey on deep learning in medical image analysis. For each decision tree, node importance is calculated using Gini importance, Eq. My education and internships have equipped me with strong technical skills in Python, deep learning models, machine learning classification, text classification, and more. For instance,\(1\times 1\) conv. Cauchemez, S. et al. The lowest accuracy was obtained by HGSO in both measures. The prey follows Weibull distribution during discovering the search space to detect potential locations of its food. \end{aligned} \end{aligned}$$, $$\begin{aligned} WF(x)=\exp ^{\left( {\frac{x}{k}}\right) ^\zeta } \end{aligned}$$, $$\begin{aligned}&Accuracy = \frac{\text {TP} + \text {TN}}{\text {TP} + \text {TN} + \text {FP} + \text {FN}} \end{aligned}$$, $$\begin{aligned}&Sensitivity = \frac{\text {TP}}{\text{ TP } + \text {FN}}\end{aligned}$$, $$\begin{aligned}&Specificity = \frac{\text {TN}}{\text {TN} + \text {FP}}\end{aligned}$$, $$\begin{aligned}&F_{Score} = 2\times \frac{\text {Specificity} \times \text {Sensitivity}}{\text {Specificity} + \text {Sensitivity}} \end{aligned}$$, $$\begin{aligned} Best_{acc} = \max _{1 \le i\le {r}} Accuracy \end{aligned}$$, $$\begin{aligned} Best_{Fit_i} = \min _{1 \le i\le r} Fit_i \end{aligned}$$, $$\begin{aligned} Max_{Fit_i} = \max _{1 \le i\le r} Fit_i \end{aligned}$$, $$\begin{aligned} \mu = \frac{1}{r} \sum _{i=1}^N Fit_i \end{aligned}$$, $$\begin{aligned} STD = \sqrt{\frac{1}{r-1}\sum _{i=1}^{r}{(Fit_i-\mu )^2}} \end{aligned}$$, https://doi.org/10.1038/s41598-020-71294-2.
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