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. Finally, the predator follows the levy flight distribution to exploit its prey location. Regarding the consuming time as in Fig. Software available from tensorflow. 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. Also, As seen in Fig. One of these datasets has both clinical and image data. Highlights COVID-19 CT classification using chest tomography (CT) images. Then the best solutions are reached which determine the optimal/relevant features that should be used to address the desired output via several performance measures. ADS Scientific Reports (Sci Rep) For each decision tree, node importance is calculated using Gini importance, Eq. The convergence behaviour of FO-MPA was evaluated over 25 independent runs and compared to other algorithms, where the x-axis and the y-axis represent the iterations and the fitness value, respectively. Internet Explorer). 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. Convolutional neural networks were implemented in Python 3 under Google Colaboratory46, commonly referred to as Google Colab, which is a research project for prototyping machine learning models on powerful hardware options such as GPUs and TPUs. The symbol \(r\in [0,1]\) represents a random number. & Mahmoud, N. Feature selection based on hybrid optimization for magnetic resonance imaging brain tumor classification and segmentation. Computer Department, Damietta University, Damietta, Egypt, Electrical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum, Egypt, State Key Laboratory for Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China, Department of Applied Informatics, Vytautas Magnus University, Kaunas, Lithuania, Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt, School of Computer Science and Robotics, Tomsk Polytechnic University, Tomsk, Russia, You can also search for this author in COVID-19 (coronavirus disease 2019) is a new viral infection disease that is widely spread worldwide. The Marine Predators Algorithm (MPA)is a recently developed meta-heuristic algorithm that emulates the relation among the prey and predator in nature37. & Zhu, Y. Kernel feature selection to fuse multi-spectral mri images for brain tumor segmentation. Average of the consuming time and the number of selected features in both datasets. The results of max measure (as in Eq. Li, H. etal. However, the proposed FO-MPA approach has an advantage in performance compared to other works. Afzali, A., Mofrad, F.B. 517 PDF Ensemble of Patches for COVID-19 X-Ray Image Classification Thiago Chen, G. Oliveira, Z. Dias Medicine How- individual class performance. 152, 113377 (2020). One from the well-know definitions of FC is the Grunwald-Letnikov (GL), which can be mathematically formulated as below40: where \(D^{\delta }(U(t))\) refers to the GL fractional derivative of order \(\delta\). FC provides a clear interpretation of the memory and hereditary features of the process. CNNs are more appropriate for large datasets. (24). Eng. Donahue, J. et al. Sci Rep 10, 15364 (2020). Use the Previous and Next buttons to navigate the slides or the slide controller buttons at the end to navigate through each slide. 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. Table2 depicts the variation in morphology of the image, lighting, structure, black spaces, shape, and zoom level among the same dataset, as well as with the other dataset. The predator tries to catch the prey while the prey exploits the locations of its food. Besides, all algorithms showed the same statistical stability in STD measure, except for HHO and HGSO. You have a passion for computer science and you are driven to make a difference in the research community? Use of chest ct in combination with negative rt-pcr assay for the 2019 novel coronavirus but high clinical suspicion. Bisong, E. Building Machine Learning and Deep Learning Models on Google Cloud Platform (Springer, Berlin, 2019). Future Gener. 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. wrote the intro, related works and prepare results. An efficient feature generation approach based on deep learning and feature selection techniques for traffic classification. Also, because COVID-19 is a virus, distinguish COVID-19 from common viral . The experimental results and comparisons with other works are presented inResults and discussion section, while they are discussed in Discussion section Finally, the conclusion is described in Conclusion section. Alhamdulillah, glad to share that our paper entitled "Multi-class classification of brain tumor types from MR Images using EfficientNets" has been accepted for (14)(15) to emulate the motion of the first half of the population (prey) and Eqs. Moreover, the Weibull distribution employed to modify the exploration function. J. Clin. CAS Design incremental data augmentation strategy for COVID-19 CT data. This dataset consists of 219 COVID-19 positive images and 1341 negative COVID-19 images. IRBM https://doi.org/10.1016/j.irbm.2019.10.006 (2019). In our experiment, we randomly split the data into 70%, 10%, and 20% for training, validation, and testing sets, respectively. Besides, the used statistical operations improve the performance of the FO-MPA algorithm because it supports the algorithm in selecting only the most important and relevant features. Correspondence to 101, 646667 (2019). Med. In this work, we have used four transfer learning models, VGG16, InceptionV3, ResNet50, and DenseNet121 for the classification tasks. Article Figure6 shows a comparison between our FO-MPA approach and other CNN architectures. Figure5 illustrates the convergence curves for FO-MPA and other algorithms in both datasets. We have used RMSprop optimizer for weight updates, cross entropy loss function and selected learning rate as 0.0001. 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. The second one is based on Matlab, where the feature selection part (FO-MPA algorithm) was performed. Then, applying the FO-MPA to select the relevant features from the images. 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. 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. Extensive evaluation experiments had been carried out with a collection of two public X-ray images datasets. Adv. They compared the BA to PSO, and the comparison outcomes showed that BA had better performance. Image Classification With ResNet50 Convolution Neural Network (CNN) on Covid-19 Radiography | by Emmanuella Anggi | The Startup | Medium 500 Apologies, but something went wrong on our end.. \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. layers is to extract features from input images. 0.9875 and 0.9961 under binary and multi class classifications respectively. Eng. Civit-Masot et al. Shi, H., Li, H., Zhang, D., Cheng, C. & Cao, X. They used different images of lung nodules and breast to evaluate their FS methods. Radiomics: extracting more information from medical images using advanced feature analysis. Based on Standard Deviation measure (STD), the most stable algorithms were SCA, SGA, BPSO, and bGWO, respectively. Experimental results have shown that the proposed Fuzzy Gabor-CNN algorithm attains highest accuracy, Precision, Recall and F1-score when compared to existing feature extraction and classification techniques. They applied a fuzzy decision tree classifier, and they found that fuzzy PSO improved the classification accuracy. The evaluation confirmed that FPA based FS enhanced classification accuracy. Duan, H. et al. Health Inf. Deep cnns for microscopic image classification by exploiting transfer learning and feature concatenation. Support Syst. MathSciNet The two datasets consist of X-ray COVID-19 images by international Cardiothoracic radiologist, researchers and others published on Kaggle. . In this paper, we used two different datasets. 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. & SHAH, S. S.H. The diagnostic evaluation of convolutional neural network (cnn) for the assessment of chest x-ray of patients infected with covid-19. In this paper, a new ML-method proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. Abbas, A., Abdelsamea, M.M. & Gaber, M.M. Classification of covid-19 in chest x-ray images using detrac deep convolutional neural network. Appl. 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). SharifRazavian, A., Azizpour, H., Sullivan, J. }\delta (1-\delta )(2-\delta ) U_{i}(t-2)\\&\quad + \frac{1}{4! A features extraction method using the Histogram of Oriented Gradients (HOG) algorithm and the Linear Support Vector Machine (SVM), K-Nearest Neighbor (KNN) Medium and Decision Tree (DT) Coarse Tree classification methods can be used in the diagnosis of Covid-19 disease. Epub 2022 Mar 3. For this motivation, we utilize the FC concept with the MPA algorithm to boost the second step of the standard version of the algorithm. Podlubny, I. Liao, S. & Chung, A. C. Feature based nonrigid brain mr image registration with symmetric alpha stable filters. Future Gener. Artif. Classification of COVID19 using Chest X-ray Images in Keras 4.6 33 ratings Share Offered By In this Guided Project, you will: Learn to Build and Train the Convolutional Neural Network using Keras with Tensorflow as Backend Learn to Visualize Data in Matplotlib Learn to make use of the Trained Model to Predict on a New Set of Data 2 hours 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. Pool layers are used mainly to reduce the inputs size, which accelerates the computation as well. Refresh the page, check Medium 's site status, or find something interesting. 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.