Johnson et al.31 applied the flower pollination algorithm (FPA) to select features from CT images of the lung, to detect lung cancers. Figure7 shows the most recent published works as in54,55,56,57 and44 on both dataset 1 and dataset 2. Table3 shows the numerical results of the feature selection phase for both datasets. 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. Eng. Extensive evaluation experiments had been carried out with a collection of two public X-ray images datasets. Sci Rep 10, 15364 (2020). This stage can be mathematically implemented as below: In Eq. Negative COVID-19 images were collected from another Chest X-ray Kaggle published dataset43. 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. Kharrat, A. 132, 8198 (2018). layers is to extract features from input images. chest X-ray images into three classes of COVID-19, normal chest X-ray and other lung diseases. Biol. While the second half of the agents perform the following equations. From Fig. 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. The results show that, using only 6 epochs for training, the CNNs achieved very high performance on the classification task. Average of the consuming time and the number of selected features in both datasets. CAS Generally, the proposed FO-MPA approach showed satisfying performance in both the feature selection ratio and the classification rate. The \(\delta\) symbol refers to the derivative order coefficient. They applied the SVM classifier with and without RDFS. They also used the SVM to classify lung CT images. 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. However, it was clear that VGG19 and MobileNet achieved the best performance over other CNNs. Performance analysis of neural networks for classification of medical images with wavelets as a feature extractor. 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. Initialize solutions for the prey and predator. 121, 103792 (2020). We have used RMSprop optimizer for weight updates, cross entropy loss function and selected learning rate as 0.0001. In general, MPA is a meta-heuristic technique that simulates the behavior of the prey and predator in nature37. 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. Tree based classifier are the most popular method to calculate feature importance to improve the classification since they have high accuracy, robustness, and simple38. By submitting a comment you agree to abide by our Terms and Community Guidelines. The MCA-based model is used to process decomposed images for further classification with efficient storage. In 2018 IEEE International Symposium on Circuits and Systems (ISCAS), 15 (IEEE, 2018). IRBM https://doi.org/10.1016/j.irbm.2019.10.006 (2019). Recently, a combination between the fractional calculus tool and the meta-heuristics opens new doors in providing robust and reliable variants41. The proposed IMF approach successfully achieves two important targets, selecting small feature numbers with high accuracy. & 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. 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. Isolation and characterization of a bat sars-like coronavirus that uses the ace2 receptor. Then, using an enhanced version of Marine Predators Algorithm to select only relevant features. Comput. Deep residual learning for image recognition. ), such as \(5\times 5\), \(3 \times 3\), \(1 \times 1\). 95, 5167 (2016). the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in where r is the run numbers. 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. Zhang, N., Ruan, S., Lebonvallet, S., Liao, Q. Artif. Medical imaging techniques are very important for diagnosing diseases. Appl. The memory terms of the prey are updated at the end of each iteration based on first in first out concept. & Dai, Q. Discriminative clustering and feature selection for brain mri segmentation. Ozturk, T. et al. Sci. MATH Intell. Regarding the consuming time as in Fig. The predator tries to catch the prey while the prey exploits the locations of its food. 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. 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. The following stage was to apply Delta variants. 42, 6088 (2017). used a dark Covid-19 network for multiple classification experiments on Covid-19 with an accuracy of 87% [ 23 ]. 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. Robertas Damasevicius. These datasets contain hundreds of frontal view X-rays and considered the largest public resource for COVID-19 image data. Figure5 illustrates the convergence curves for FO-MPA and other algorithms in both datasets. Heidari, A. Propose similarity regularization for improving C. Therefore, several pre-trained models have won many international image classification competitions such as VGGNet24, Resnet25, Nasnet26, Mobilenet27, Inception28 and Xception29. How- individual class performance. EMRes-50 model . Also, they require a lot of computational resources (memory & storage) for building & training. With the help of numerous algorithms in AI, modern COVID-19 cases can be detected and managed in a classified framework. arXiv preprint arXiv:1711.05225 (2017). & Cmert, Z. Med. Comput. Coronavirus Disease (COVID-19): A primer for emergency physicians (2020) Summer Chavez et al. 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. For general case based on the FC definition, the Eq. Therefore in MPA, for the first third of the total iterations, i.e., \(\frac{1}{3}t_{max}\)). 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 . }\delta (1-\delta )(2-\delta )(3-\delta ) U_{i}(t-3) + P.R\bigotimes S_i. 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. Epub 2022 Mar 3. FCM reinforces the ANFIS classification learning phase based on the features of COVID-19 patients. & Baby, C.J. Emphysema medical image classification using fuzzy decision tree with fuzzy particle swarm optimization clustering. arXiv preprint arXiv:2003.13145 (2020). 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. Hence, it was discovered that the VGG-16 based DTL model classified COVID-19 better than the VGG-19 based DTL model. The proposed IFM approach is summarized as follows: Extracting deep features from Inception, where about 51 K features were extracted. Google Scholar. PubMed and JavaScript. Also, image segmentation can extract critical features, including the shape of tissues, and texture5,6. In our experiment, we randomly split the data into 70%, 10%, and 20% for training, validation, and testing sets, respectively. They achieved 98.08 % and 96.51 % of accuracy and F-Score, respectively compared to our approach with 98.77 % and 98.2% for accuracy and F-Score, respectively. Refresh the page, check Medium 's site status, or find something interesting. Currently, a new coronavirus, called COVID-19, has spread to many countries, with over two million infected people or so-called confirmed cases. However, the proposed FO-MPA approach has an advantage in performance compared to other works. A. arXiv preprint arXiv:2003.11597 (2020). In Proceedings of the IEEE Conference on computer vision and pattern recognition workshops, 806813 (2014). Improving the ranking quality of medical image retrieval using a genetic feature selection method. 2 (right). In this subsection, a comparison with relevant works is discussed. They used different images of lung nodules and breast to evaluate their FS methods. Wu, Y.-H. etal. Howard, A.G. etal. arXiv preprint arXiv:1704.04861 (2017). 79, 18839 (2020). For each decision tree, node importance is calculated using Gini importance, Eq. 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]. The evaluation confirmed that FPA based FS enhanced classification accuracy. & Pouladian, M. Feature selection for contour-based tuberculosis detection from chest x-ray images. Based on Standard Deviation measure (STD), the most stable algorithms were SCA, SGA, BPSO, and bGWO, respectively. (22) can be written as follows: By using the discrete form of GL definition of Eq. Efficient classification of white blood cell leukemia with improved swarm optimization of deep features. As seen in Fig. Health Inf. Whereas, FO-MPA, MPA, HGSO, and WOA showed similar STD results. 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. Automated detection of covid-19 cases using deep neural networks with x-ray images. Adv. Classification Covid-19 X-Ray Images | by Falah Gatea | Medium 500 Apologies, but something went wrong on our end. 4b, FO-MPA algorithm selected successfully fewer features than other algorithms, as it selected 130 and 86 features from Dataset 1 and Dataset 2, respectively. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. In this paper, we used two different datasets. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770778 (2016). Imaging 35, 144157 (2015). Detection of lung cancer on chest ct images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. Also, As seen in Fig. Pool layers are used mainly to reduce the inputs size, which accelerates the computation as well. Ozturk et al. Kong, Y., Deng, Y. 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. 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). volume10, Articlenumber:15364 (2020) This task is achieved by FO-MPA which randomly generates a set of solutions, each of them represents a subset of potential features. Finally, the sum of the features importance value on each tree is calculated then divided by the total number of trees as in Eq. The variants of concern are Alpha, Beta, Gamma, and than the COVID-19 images. Eq. I am passionate about leveraging the power of data to solve real-world problems. Contribute to hellorp1990/Covid-19-USF development by creating an account on GitHub. CAS 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). Lambin, P. et al. In COVID19 triage, DB-YNet is a promising tool to assist physicians in the early identification of COVID19 infected patients for quick clinical interventions. Mirjalili, S. & Lewis, A. Purpose The study aimed at developing an AI . The conference was held virtually due to the COVID-19 pandemic. Mobilenets: Efficient convolutional neural networks for mobile vision applications. 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 arXiv preprint arXiv:2003.13815 (2020). Cauchemez, S. et al. Key Definitions. 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. Li, H. etal. Lilang Zheng, Jiaxuan Fang, Xiaorun Tang, Hanzhang Li, Jiaxin Fan, Tianyi Wang, Rui Zhou, Zhaoyan Yan. 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. In57, ResNet-50 CNN has been applied after applying horizontal flipping, random rotation, random zooming, random lighting, and random wrapping on raw images. COVID-19 image classification using deep features and fractional-order marine predators algorithm Authors. 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. Med. (20), \(FAD=0.2\), and W is a binary solution (0 or 1) that corresponded to random solutions. The memory properties of Fc calculus makes it applicable to the fields that required non-locality and memory effect. 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. \(r_1\) and \(r_2\) are the random index of the prey. The symbol \(r\in [0,1]\) represents a random number. 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. Article In some cases (as exists in this work), the dataset is limited, so it is not sufficient for building & training a CNN. Also, other recent published works39, who combined a CNN architecture with Weighted Symmetric Uncertainty (WSU) to select optimal features for traffic classification. Imaging Syst. Li, J. et al. and M.A.A.A. 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. 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. 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.. et al. We are hiring! Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. Then, applying the FO-MPA to select the relevant features from the images. Whereas, the slowest and the insufficient convergences were reported by both SGA and WOA in Dataset 1 and by SGA in Dataset 2. . Li et al.34 proposed a self-adaptive bat algorithm (BA) to address two problems in lung X-ray images, rebalancing, and feature selection. Chollet, F. Xception: Deep learning with depthwise separable convolutions. Nature 503, 535538 (2013). The algorithm combines the assessment of image quality, digital image processing and deep learning for segmentation of the lung tissues and their classification. In Future of Information and Communication Conference, 604620 (Springer, 2020). In14, the authors proposed an FS method based on a convolutional neural network (CNN) to detect pneumonia from lung X-ray images. Compared to59 which is one of the most recent published works on X-ray COVID-19, a combination between You Only Look Once (YOLO) which is basically a real time object detection system and DarkNet as a classifier was proposed. There are three main parameters for pooling, Filter size, Stride, and Max pool. COVID-19 (coronavirus disease 2019) is a new viral infection disease that is widely spread worldwide. The . In 2019 26th National and 4th International Iranian Conference on Biomedical Engineering (ICBME), 194198 (IEEE, 2019). In Medical Imaging 2020: Computer-Aided Diagnosis, vol. 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. Table2 shows some samples from two datasets. For instance,\(1\times 1\) conv. Image Anal. Med. COVID-19 image classification using deep learning: Advances, challenges and opportunities COVID-19 image classification using deep learning: Advances, challenges and opportunities Comput Biol Med. Eng. 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. Also, because COVID-19 is a virus, distinguish COVID-19 from common viral . Early diagnosis, timely treatment, and proper confinement of the infected patients are some possible ways to control the spreading of . A.T.S. In Inception, there are different sizes scales convolutions (conv. 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/ 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. 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. It also shows that FO-MPA can select the smallest subset of features, which reflects positively on performance. They employed partial differential equations for extracting texture features of medical images. Stage 2: The prey/predator in this stage begin exploiting the best location that detects for their foods. Math. Comput. Figure3 illustrates the structure of the proposed IMF approach. \(Fit_i\) denotes a fitness function value. Internet Explorer). Comparison with other previous works using accuracy measure. Figure6 shows a comparison between our FO-MPA approach and other CNN architectures. & Cao, J. For example, Lambin et al.7 proposed an efficient approach called Radiomics to extract medical image features. Syst. It is calculated between each feature for all classes, as in Eq. Syst. The lowest accuracy was obtained by HGSO in both measures. ADS Expert Syst. E. B., Traina-Jr, C. & Traina, A. J. FP (false positives) are the positive COVID-19 images that were incorrectly labeled as negative COVID-19, while FN (false negatives) are the negative COVID-19 images that were mislabeled as positive COVID-19 images. Al-qaness, M. A., Ewees, A. It classifies the chest X-ray images into three categories that includes Covid-19, Pneumonia and normal. Podlubny, I. 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. Image Underst. <span> <h5>Background</h5> <p>The COVID19 pandemic has precipitated global apprehensions about increased fatalities and raised concerns about gaps in healthcare . Correspondence to Int. Sci. Very deep convolutional networks for large-scale image recognition. A., Fan, H. & Abd ElAziz, M. Optimization method for forecasting confirmed cases of covid-19 in china. Access through your institution. Google Scholar. Generally, the most stable algorithms On dataset 1 are WOA, SCA, HGSO, FO-MPA, and SGA, respectively. Phys. In this paper, filters of size 2, besides a stride of 2 and \(2 \times 2\) as Max pool, were adopted. Introduction Deep Learning Based Image Classification of Lungs Radiography for Detecting COVID-19 using a Deep CNN and ResNet 50 Bukhari, S. U.K., Bukhari, S. S.K., Syed, A. COVID-19 image classification using deep features and fractional-order marine predators algorithm. Going deeper with convolutions. Evaluation outcomes showed that GA based FS methods outperformed traditional approaches, such as filter based FS and traditional wrapper methods. Google Scholar. Liao, S. & Chung, A. C. Feature based nonrigid brain mr image registration with symmetric alpha stable filters. used VGG16 to classify Covid-19 and achieved good results with an accuracy of 86% [ 22 ]. 78, 2091320933 (2019). \(\Gamma (t)\) indicates gamma function. & Cmert, Z. One of the main disadvantages of our approach is that its built basically within two different environments. 9, 674 (2020). Litjens, G. et al. Finally, the predator follows the levy flight distribution to exploit its prey location. Multimedia Tools Appl. \(\bigotimes\) indicates the process of element-wise multiplications. My education and internships have equipped me with strong technical skills in Python, deep learning models, machine learning classification, text classification, and more. In Dataset 2, FO-MPA also is reported as the highest classification accuracy with the best and mean measures followed by the BPSO. J. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. In this subsection, the results of FO-MPA are compared against most popular and recent feature selection algorithms, such as Whale Optimization Algorithm (WOA)49, Henry Gas Solubility optimization (HGSO)50, Sine cosine Algorithm (SCA), Slime Mould Algorithm (SMA)51, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO)52, Harris Hawks Optimization (HHO)53, Genetic Algorithm (GA), and basic MPA. Sahlol, A.T., Yousri, D., Ewees, A.A. et al. Vis. In this subsection, the performance of the proposed COVID-19 classification approach is compared to other CNN architectures. Although outbreaks of SARS and MERS had confirmed human to human transmission3, they had not the same spread speed and infection power of the new coronavirus (COVID-19). (15) can be reformulated to meet the special case of GL definition of Eq. Therefore, in this paper, we propose a hybrid classification approach of COVID-19. A.A.E. Both datasets shared some characteristics regarding the collecting sources. Get the most important science stories of the day, free in your inbox. You have a passion for computer science and you are driven to make a difference in the research community? 2020-09-21 . However, it has some limitations that affect its quality. The whale optimization algorithm. & Mirjalili, S. Slime mould algorithm: A new method for stochastic optimization. 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. Wish you all a very happy new year ! 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. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition12511258 (2017). The Weibull Distribution is a heavy-tied distribution which presented as in Fig. The next process is to compute the performance of each solution using fitness value and determine which one is the best solution. Faramarzi, A., Heidarinejad, M., Mirjalili, S. & Gandomi, A. H. Marine predators algorithm: a nature-inspired metaheuristic. Future Gener. 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 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 19 (2015). Coronavirus disease (Covid-19) is an infectious disease that attacks the respiratory area caused by the severe acute . Propose a novel robust optimizer called Fractional-order Marine Predators Algorithm (FO-MPA) to select efficiently the huge feature vector produced from the CNN. A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models. They are distributed among people, bats, mice, birds, livestock, and other animals1,2. The largest features were selected by SMA and SGA, respectively. Moreover, we design a weighted supervised loss that assigns higher weight for . Metric learning Metric learning can create a space in which image features within the. 97, 849872 (2019). Donahue, J. et al. (18)(19) for the second half (predator) as represented below. Springer Science and Business Media LLC Online. Inceptions layer details and layer parameters of are given in Table1. The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. They applied the SVM classifier for new MRI images to segment brain tumors, automatically. We adopt a special type of CNN called a pre-trained model where the network is previously trained on the ImageNet dataset, which contains millions of variety of images (animal, plants, transports, objects,..) on 1000 classe categories. They showed that analyzing image features resulted in more information that improved medical imaging. Nguyen, L.D., Lin, D., Lin, Z. After feature extraction, we applied FO-MPA to select the most significant features. The predator uses the Weibull distribution to improve the exploration capability. is applied before larger sized kernels are applied to reduce the dimension of the channels, which accordingly, reduces the computation cost. Figure5, shows that FO-MPA shows an efficient and faster convergence than the other optimization algorithms on both datasets. 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. Apostolopoulos, I. D. & Mpesiana, T. A. Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Harikumar, R. & Vinoth Kumar, B. 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. implemented the FO-MPA swarm optimization and prepared the related figures and manuscript text. Alhamdulillah, glad to share that our paper entitled "Multi-class classification of brain tumor types from MR Images using EfficientNets" has been accepted for In the current work, the values of k, and \(\zeta\) are set to 2, and 2, respectively. J. Med. 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. Also, in12, an Fs method based on SVM was proposed to detect Alzheimers disease from SPECT images. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 2. In Eq. The proposed IMF approach is employed to select only relevant and eliminate unnecessary features. Multimedia Tools Appl. Stage 2 has been executed in the second third of the total number of iterations when \(\frac{1}{3}t_{max}< t< \frac{2}{3}t_{max}\).