Whereas, the slowest and the insufficient convergences were reported by both SGA and WOA in Dataset 1 and by SGA in Dataset 2. Authors 11, 243258 (2007). 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. where \(REfi_{i}\) represents the importance of feature i that were calculated from all trees, where \(normfi_{ij}\) is the normalized feature importance for feature i in tree j, also T is the total number of trees. Introduction Eng. Corona Virus lung infected X-Ray Images accessible by Kaggle a complete 590 images, which classified in 2 classes: typical and Covid-19. 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. Number of extracted feature and classification accuracy by FO-MPA compared to other CNNs on dataset 1 (left) and on dataset 2 (right). Article A. They are distributed among people, bats, mice, birds, livestock, and other animals1,2. Li, H. etal. In this paper, we used two different datasets. Dhanachandra, N. & Chanu, Y. J. They applied a fuzzy decision tree classifier, and they found that fuzzy PSO improved the classification accuracy. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770778 (2016). (22) can be written as follows: By taking into account the early mentioned relation in Eq. A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models. To further analyze the proposed algorithm, we evaluate the selected features by FO-MPA by performing classification. Based on Standard Deviation measure (STD), the most stable algorithms were SCA, SGA, BPSO, and bGWO, respectively. Also, WOA algorithm showed good results in all measures, unlike dataset 1, which can conclude that no algorithm can solve all kinds of problems. The second one is based on Matlab, where the feature selection part (FO-MPA algorithm) was performed. 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). 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. Accordingly, the prey position is upgraded based the following equations. 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. 41, 923 (2019). In54, AlexNet pre-trained network was used to extract deep features then applied PCA to select the best features by eliminating highly correlated features. (24). However, using medical imaging, chest CT, and chest X-ray scan can play a critical role in COVID-19 diagnosis. FC provides a clear interpretation of the memory and hereditary features of the process. Al-qaness, M. A., Ewees, A. Internet Explorer). (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. You have a passion for computer science and you are driven to make a difference in the research community? Then, applying the FO-MPA to select the relevant features from the images. 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. They shared some parameters, such as the total number of iterations and the number of agents which were set to 20 and 15, respectively. In our experiment, we randomly split the data into 70%, 10%, and 20% for training, validation, and testing sets, respectively. 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. where \(fi_{i}\) represents the importance of feature I, while \(ni_{j}\) refers to the importance of node j. Accordingly, the FC is an efficient tool for enhancing the performance of the meta-heuristic algorithms by considering the memory perspective during updating the solutions. Syst. Therefore in MPA, for the first third of the total iterations, i.e., \(\frac{1}{3}t_{max}\)). Ge, X.-Y. Knowl. Thank you for visiting nature.com. For both datasets, the Covid19 images were collected from patients with ages ranging from 40-84 from both genders. However, some of the extracted features by CNN might not be sufficient, which may affect negatively the quality of the classification 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. CAS Tensorflow: Large-scale machine learning on heterogeneous systems, 2015. Deep residual learning for image recognition. Eq. For example, Da Silva et al.30 used the genetic algorithm (GA) to develop feature selection methods for ranking the quality of medical images. Ozturk et al. They employed partial differential equations for extracting texture features of medical 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. & Wang, W. Medical image segmentation using fruit fly optimization and density peaks clustering. Recombinant: A process in which the genomes of two SARS-CoV-2 variants (that have infected a person at the same time) combine during the viral replication process to form a new variant that is different . Huang, P. et al. Article 9, 674 (2020). In this subsection, the performance of the proposed COVID-19 classification approach is compared to other CNN architectures. The focus of this study is to evaluate and examine a set of deep learning transfer learning techniques applied to chest radiograph images for the classification of COVID-19, normal (healthy), and pneumonia. 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. is applied before larger sized kernels are applied to reduce the dimension of the channels, which accordingly, reduces the computation cost. On January 20, 2023, Japanese Prime Minister Fumio Kishida announced that the country would be downgrading the COVID-19 classification. 2022 May;144:105350. doi: 10.1016/j.compbiomed.2022.105350. They were also collected frontal and lateral view imagery and metadata such as the time since first symptoms, intensive care unit (ICU) status, survival status, intubation status, or hospital location. arXiv preprint arXiv:2003.13145 (2020). 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 . (14)-(15) are implemented in the first half of the agents that represent the exploitation. 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 . Table2 shows some samples from two datasets. The algorithm combines the assessment of image quality, digital image processing and deep learning for segmentation of the lung tissues and their classification. Extensive evaluation experiments had been carried out with a collection of two public X-ray images datasets. Eurosurveillance 18, 20503 (2013). (15) can be reformulated to meet the special case of GL definition of Eq. Furthermore, deep learning using CNN is considered one of the best choices in medical imaging applications20, especially classification. Decis. Scientific Reports (Sci Rep) Based on54, the later step reduces the memory requirements, and improve the efficiency of the framework. 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 . Layers are applied to extract different types of features such as edges, texture, colors, and high-lighted patterns from the images. Credit: NIAID-RML 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. 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. Lambin, P. et al. 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. Fung, G. & Stoeckel, J. Svm feature selection for classification of spect images of alzheimers disease using spatial information. Health Inf. 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. FCM reinforces the ANFIS classification learning phase based on the features of COVID-19 patients. Robertas Damasevicius. Methods Med. Acharya et al.11 applied different FS methods to classify Alzheimers disease using MRI images. The results are the best achieved compared to other CNN architectures and all published works in the same datasets. In this subsection, a comparison with relevant works is discussed. 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. J. Faramarzi et al.37 divided the agents for two halves and formulated Eqs. A. For instance,\(1\times 1\) conv. Eur. 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. 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). 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. While, MPA, BPSO, SCA, and SGA obtained almost the same accuracy, followed by both bGWO, WOA, and SMA. In this paper, Inception is applied as a feature extractor, where the input image shape is (229, 229, 3). Memory FC prospective concept (left) and weibull distribution (right). 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. Performance analysis of neural networks for classification of medical images with wavelets as a feature extractor. 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. 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.