A.A.E. 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. Tree based classifier are the most popular method to calculate feature importance to improve the classification since they have high accuracy, robustness, and simple38. Inceptions layer details and layer parameters of are given in Table1. For fair comparison, each algorithms was performed (run) 25 times to produce statistically stable results.The results are listed in Tables3 and4. Furthermore, deep learning using CNN is considered one of the best choices in medical imaging applications20, especially classification. An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm. This stage can be mathematically implemented as below: In Eq. Abadi, M. et al. . J. Clin. arXiv preprint arXiv:2004.07054 (2020). In Eq. 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. 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. Shi, H., Li, H., Zhang, D., Cheng, C. & Cao, X. Johnson et al.31 applied the flower pollination algorithm (FPA) to select features from CT images of the lung, to detect lung cancers. FCM reinforces the ANFIS classification learning phase based on the features of COVID-19 patients. In order to normalize the values between 0 and 1 by dividing by the sum of all feature importance values, as in Eq. 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. Moreover, we design a weighted supervised loss that assigns higher weight for . Softw. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Also, because COVID-19 is a virus, distinguish COVID-19 from common viral . One of these datasets has both clinical and image data. where CF is the parameter that controls the step size of movement for the predator. A properly trained CNN requires a lot of data and CPU/GPU time. ADS 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. The main purpose of Conv. https://doi.org/10.1016/j.future.2020.03.055 (2020). 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. and JavaScript. All classication models ever, the virus mutates, and new variants emerge and dis- performed better in classifying the Non-COVID-19 images appear. 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. Automated detection of covid-19 cases using deep neural networks with x-ray images. A. Automated detection of alzheimers disease using brain mri imagesa study with various feature extraction techniques. IRBM https://doi.org/10.1016/j.irbm.2019.10.006 (2019). Computational image analysis techniques play a vital role in disease treatment and diagnosis. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from . Ge, X.-Y. Lett. \(r_1\) and \(r_2\) are the random index of the prey. In 2018 IEEE International Symposium on Circuits and Systems (ISCAS), 15 (IEEE, 2018). \end{aligned}$$, $$\begin{aligned} U_i(t+1)-U_i(t)=P.R\bigotimes S_i \end{aligned}$$, $$\begin{aligned} D ^{\delta } \left[ U_{i}(t+1)\right] =P.R\bigotimes S_i \end{aligned}$$, $$D^{\delta } \left[ {U_{i} (t + 1)} \right] = U_{i} (t + 1) + \sum\limits_{{k = 1}}^{m} {\frac{{( - 1)^{k} \Gamma (\delta + 1)U_{i} (t + 1 - k)}}{{\Gamma (k + 1)\Gamma (\delta - k + 1)}}} = P \cdot R \otimes S_{i} .$$, $$\begin{aligned} \begin{aligned} U(t+1)_{i}= - \sum _{k=1}^{m} \frac{(-1)^k\Gamma (\delta +1)U_{i}(t+1-k)}{\Gamma (k+1)\Gamma (\delta -k+1)} + P.R\bigotimes S_i. Automatic CNN-based Chest X-Ray (CXR) image classification for detecting Covid-19 attracted so much attention. (2) calculated two child nodes. In Dataset 2, FO-MPA also is reported as the highest classification accuracy with the best and mean measures followed by the BPSO. Table2 shows some samples from two datasets. I am passionate about leveraging the power of data to solve real-world problems. Moreover, the Weibull distribution employed to modify the exploration function. To survey the hypothesis accuracy of the models. In our example the possible classifications are covid, normal and pneumonia. However, the proposed IMF approach achieved the best results among the compared algorithms in least time. Chollet, F. Keras, a python deep learning library. While55 used different CNN structures. Comput. All authors discussed the results and wrote the manuscript together. Radiology 295, 2223 (2020). The whale optimization algorithm. It classifies the chest X-ray images into three categories that includes Covid-19, Pneumonia and normal. 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. Multimedia Tools Appl. Bisong, E. Building Machine Learning and Deep Learning Models on Google Cloud Platform (Springer, Berlin, 2019). 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. MathSciNet & Cao, J. Extensive evaluation experiments had been carried out with a collection of two public X-ray images datasets. You have a passion for computer science and you are driven to make a difference in the research community? My education and internships have equipped me with strong technical skills in Python, deep learning models, machine learning classification, text classification, and more. \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. In this experiment, the selected features by FO-MPA were classified using KNN. arXiv preprint arXiv:2004.05717 (2020). 42, 6088 (2017). The evaluation confirmed that FPA based FS enhanced classification accuracy. Thank you for visiting nature.com. Get the most important science stories of the day, free in your inbox. For example, as our input image has the shape \(224 \times 224 \times 3\), Nasnet26 produces 487 K features, Resnet25 and Xception29 produce about 100 K features and Mobilenet27 produces 50 K features, while FO-MPA produces 130 and 86 features for both dataset1 and dataset 2, respectively. In54, AlexNet pre-trained network was used to extract deep features then applied PCA to select the best features by eliminating highly correlated features. The model was developed using Keras library47 with Tensorflow backend48. The MPA starts with the initialization phase and then passing by other three phases with respect to the rational velocity among the prey and the predator. (33)), showed that FO-MPA also achieved the best value of the fitness function compared to others. Hashim, F. A., Houssein, E. H., Mabrouk, M. S., Al-Atabany, W. & Mirjalili, S. Henry gas solubility optimization: a novel physics-based algorithm. Detection of lung cancer on chest ct images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. 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/]. (22) can be written as follows: By taking into account the early mentioned relation in Eq. Feature selection based on gaussian mixture model clustering for the classification of pulmonary nodules based on computed tomography. IEEE Trans. The \(\delta\) symbol refers to the derivative order coefficient. J. Med. (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. Very deep convolutional networks for large-scale image recognition. Deep cnns for microscopic image classification by exploiting transfer learning and feature concatenation. Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs. The shape of the output from the Inception is (5, 5, 2048), which represents a feature vector of size 51200. (20), \(FAD=0.2\), and W is a binary solution (0 or 1) that corresponded to random solutions. Nature 503, 535538 (2013). Eng. Diagnosis of parkinsons disease with a hybrid feature selection algorithm based on a discrete artificial bee colony. Future Gener. Chong et al.8 proposed an FS model, called Robustness-Driven FS (RDFS) to select futures from lung CT images to classify the patterns of fibrotic interstitial lung diseases. They used K-Nearest Neighbor (kNN) to classify x-ray images collected from Montgomery dataset, and it showed good performances. Book In Proceedings of the IEEE Conference on computer vision and pattern recognition workshops, 806813 (2014). 43, 635 (2020). 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. For the image features that led to categorization, there were varied levels of agreement in the interobserver and intraobserver components that . 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. You are using a browser version with limited support for CSS. Med. Huang, P. et al. Accordingly, the prey position is upgraded based the following equations. 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. 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). This dataset consists of 219 COVID-19 positive images and 1341 negative COVID-19 images. Appl. 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. https://doi.org/10.1155/2018/3052852 (2018). There are three main parameters for pooling, Filter size, Stride, and Max pool. Pool layers are used mainly to reduce the inputs size, which accelerates the computation as well. This combination should achieve two main targets; high performance and resource consumption, storage capacity which consequently minimize processing time. One of the main disadvantages of our approach is that its built basically within two different environments. Syst. Szegedy, C. et al. Provided by the Springer Nature SharedIt content-sharing initiative, Environmental Science and Pollution Research (2023), Archives of Computational Methods in Engineering (2023), Arabian Journal for Science and Engineering (2023). In9, to classify ultrasound medical images, the authors used distance-based FS methods and a Fuzzy Support Vector Machine (FSVM). Two real datasets about COVID-19 patients are studied in this paper. Furthermore, using few hundreds of images to build then train Inception is considered challenging because deep neural networks need large images numbers to work efficiently and produce efficient features. Average of the consuming time and the number of selected features in both datasets. Liao, S. & Chung, A. C. Feature based nonrigid brain mr image registration with symmetric alpha stable filters. arXiv preprint arXiv:2003.13815 (2020). The proposed approach was evaluated on two public COVID-19 X-ray datasets which achieves both high performance and reduction of computational complexity. & Carlsson, S. Cnn features off-the-shelf: an astounding baseline for recognition. Eng. 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. Sahlol, A. T., Kollmannsberger, P. & Ewees, A. 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. Moreover, the \(R_B\) parameter has been changed to depend on weibull distribution as described below. Regarding the consuming time as in Fig. In the meantime, to ensure continued support, we are displaying the site without styles Some people say that the virus of COVID-19 is. Comput. Wu, Y.-H. etal. Knowl. Although convolutional neural networks (CNNs) is considered the current state-of-the-art image classification technique, it needs massive computational cost for deployment and training. In57, ResNet-50 CNN has been applied after applying horizontal flipping, random rotation, random zooming, random lighting, and random wrapping on raw images. J. Li et al.34 proposed a self-adaptive bat algorithm (BA) to address two problems in lung X-ray images, rebalancing, and feature selection. where r is the run numbers. . Design incremental data augmentation strategy for COVID-19 CT data. Robertas Damasevicius. They also used the SVM to classify lung CT images. & Cmert, Z. Health Inf. Whereas the worst one was SMA algorithm. The results are the best achieved on these datasets when compared to a set of recent feature selection algorithms.

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