Google Scholar. Then, using an enhanced version of Marine Predators Algorithm to select only relevant features. Meanwhile, the prey moves effectively based on its memory for the previous events to catch its food, as presented in Eq. Ren, K., Hong, G., Chen, X. et al. 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 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. Image Underst. 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. The above datasets were annotated by hospital experts in a scientific and rigorous manner. Rep. 10, 1–11 (2020). Therefore in MPA, for the first third of the total iterations, i.e., \(\frac{1}{3}t_{max}\)). ADS  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. You are using a browser version with limited support for CSS. Cai et al.18 proposed Multi-MedVit, a COVID-19 diagnostic framework based on multi-input transformer, and demonstrated that multi-scale data input enhanced data helps improve model stability. 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. Dosovitskiy, A. et al. Also, all other works do not give further statistics about their model’s complexity and the number of featurset produced, unlike, our approach which extracts the most informative features (130 and 86 features for dataset 1 and dataset 2) that imply faster computation time and, accordingly, lower resource consumption. where \(R_L\) has random numbers that follow Lévy distribution. Aslan, M. F., Sabanci, K., Durdu, A. WebBoth the model uses Lungs CT Scan images to classify the covid-19. Multimedia Tools Appl. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. Kharrat, A. Automatic classification is an important, engaging, and challenging research topic in image processing and pattern recognition, especially in medical applications for the … A weakly-supervised framework for covid-19 classification and lesion localization from chest ct. IEEE Trans. Apostolopoulos, I. D. & Mpesiana, T. A. Covid-19: Automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. where \(W_A \in R^{C\times L}\) forms semantic groups from X,\(SoftMax(\cdot )\) is the softmax activation function,Xrepresents the feature map. Biocybern. It can be expressed by the formula Eq. arXiv preprint arXiv:1409.1556 (2014). Chollet, F. Keras, a python deep learning library. Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily. Rajpurkar, P. et al. Comput. He used all the output from the encoder to achieve better results, and demonstrated that the transformer-based model was better than CNN at Covid-19 identification. GitHub - youngsoul/pyimagesearch-covid19-image-classification: … where r is the run numbers. However, it has some limitations that affect its quality. COVID-19 FC provides a clear interpretation of the memory and hereditary features of the process. Research on cervical cancer image recognition method based on i-capsnet. Pathak et al.12 used transfer learning to classify COVID-19. Among the FS methods, the metaheuristic techniques have been established their performance overall other FS methods when applied to classify medical images. In the meantime, to ensure continued support, we are displaying the site without styles Radiology 296, E65–E71 (2020). Appl. Memory FC prospective concept (left) and weibull distribution (right). Cite this article. However, WOA showed the worst performances in these measures; which means that if it is run in the same conditions several times, the same results will be obtained. Toğaçar, M., Ergen, B. Vis. Sci. https://www.sirm.org/category/senza-categoria/covid-19/ (2020). Podlubny, I. In general, MPA is a meta-heuristic technique that simulates the behavior of the prey and predator in nature37. Wu, F., Yuan, J., Li, Y., Li, J. Imaging Syst. Barstugan et al. The DRE-Net model performed binary classification experiment (COVID-19 and bacterial pneumonia) on 1485 CT images. Higher Technological Institute, Biomedical Engineering Department, Egypt 1 author 2. Soft Comput. All images of the healthy subjects were taken from the COVID-19 … COVID-19 lung CT image segmentation using deep learning … Article  Finally, the sum of the feature’s importance value on each tree is calculated then divided by the total number of trees as in Eq. Formally, we can define it as Eq. Future Gener. 11314, 113142S (International Society for Optics and Photonics, 2020). Chen, J. et al. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. In this paper, Inception is applied as a feature extractor, where the input image shape is (229, 229, 3). The combination of Conv. }\delta (1-\delta ) U_{i}(t-1)+ \frac{1}{3! 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). In Medical Imaging 2020: Computer-Aided Diagnosis, vol. (3). Toğaçar, M., Ergen, B. The detection speed of RMTNet is improved by 60.3% compared with ResNet, 47.4% compared with VGGNet-16, 28.8% compared with i-CapsNet, and 2.6% compared with MGMADS-3. Abbas, A., Abdelsamea, M. M. & Gaber, M. M. Classification of covid-19 in chest x-ray images using detrac deep convolutional neural network. 121, 103792 (2020). Comput. Rep. 10, 1–12 (2020). J. wrote the intro, related works and prepare results. Li, J. et al. 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. Liao, S. & Chung, A. C. Feature based nonrigid brain mr image registration with symmetric alpha stable filters. Different from VIT, VT first uses convolutional layer to extract the underlying features. Sahlol, A. T., Kollmannsberger, P. & Ewees, A. 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. It is proved that the model can detect and classify COVID-19 with higher accuracy and efficiency. In Workshop on Healthcare AI and COVID-19, 11–20 (PMLR, 2022). PubMed Central  Convolutional Neural Networks (CNN) are widely used to diagnose COVID-19 pneumonia classification on Chest radiographic images to help radiologists in medical … In Iberian Conference on Pattern Recognition and Image Analysis, 176–183 (Springer, 2011). Projecting these visual tokens into the pixel space to obtain the enhanced feature map. 126, 104037 (2020). The evaluation confirmed that FPA based FS enhanced classification accuracy. 12, 310 (2022). COVID-19 image classification techniques in medical analysis using … Acharya et al.11 applied different FS methods to classify Alzheimer’s disease using MRI images. Comparison with other previous works using accuracy measure. 114, 107747 (2021). The VT module consists of three steps: (1) Group the features into different semantic concepts to generate a compact set of visual tokens. Int. Eng. 51, 810–820 (2011). We will be using the associated radiological findings of the CT scans as labels to build a classifier to predict presence of viral pneumonia. The two datasets consist of X-ray COVID-19 images by international Cardiothoracic radiologist, researchers and others published on Kaggle. In general, high specificity means a low rate of misdiagnosis, and high sensitivity means a low rate of missed diagnosis. They concluded that the hybrid method outperformed original fuzzy c-means, and it had less sensitive to noises. COVID-19 classification of X-ray images using deep neural networks To further analyze the proposed algorithm, we evaluate the selected features by FO-MPA by performing classification. Ozturk, T. et al. Sci. Wu, B. et al. On CT image data, the detection speed are 10.37 ms, 7.83 ms, 5.79 ms, 4.23 ms and 4.12 ms. Each head outputs a sequence of size X, and then concatenates the h sequences into an \(n\times d\) sequence, as the output of LMHSA. Such methods might play a significant role as a computer-aided tool for image-based clinical diagnosis soon. PubMed  CNNs are more appropriate for large datasets. The RMT-Net structure is shown in Fig. As a basic building block for processing the input data, Stem can preprocess the feature information of the input image, including segmentation, spatial dimension reduction, feature linear transformation and so on. \(W_Q \in R^{C\times C}\) ,\(W_K \in R^{C\times C}\) represents the learning weight of Q and K. The result of the multiplication of K and Q determines how the information from visual tokens is projected into the original feature map. According to the promising results of the proposed model, that combines CNN as a feature extractor and FO-MPA as a feature selector could be useful and might be successful in being applied in other image classification tasks. Deep learning-based automated COVID-19 classification from … COVID-19 image classification using deep features and fractional … Stage 2: The prey/predator in this stage begin exploiting the best location that detects for their foods. Szegedy, C. et al. In54, AlexNet pre-trained network was used to extract deep features then applied PCA to select the best features by eliminating highly correlated features. Oulefki, A. et al. Biol. Harikumar, R. & Vinoth Kumar, B. 9, 674 (2020). Covid-widenet-a capsule network for covid-19 detection. Intell. Due to transformer cannot transform the scale of feature map, patch aggregation is adopted to construct downsampling to realize the hierarchical structure of the network. The proposed approach was evaluated on two public COVID-19 X-ray datasets which achieves both high performance and reduction of computational complexity. Biol. This stage can be mathematically implemented as below: In Eq. volume 13, Article number: 5359 (2023) Oulefki, A., Agaian, S., Trongtirakul, T. & Laouar, A. K. Automatic covid-19 lung infected region segmentation and measurement using ct-scans images. With the deepening of the network, the number of features gradually increases. Therefore, VIT is used for global feature inference in Stage 1. and pool layers, three fully connected layers, the last one performs classification. 42, 60–88 (2017). Johnson et al.31 applied the flower pollination algorithm (FPA) to select features from CT images of the lung, to detect lung cancers. As can be seen from Table 3, the model size of RMT-Net is about 40M, which is smaller than the other four models. On this Background, Many deep learning methods have been used to diagnose COVID-19. In International Symposium on Intelligence Computation and Applications, 461–471 (Springer, 2009). Google Scholar. In this subsection, a comparison with relevant works is discussed. 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. Scientific Reports (Sci Rep) A multi-scale gated multi-head attention depthwise separable CNN model for recognizing COVID-19, Detection and analysis of COVID-19 in medical images using deep learning techniques, Feature fusion based VGGFusionNet model to detect COVID-19 patients utilizing computed tomography scan images, Towards robust diagnosis of COVID-19 using vision self-attention transformer, COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images, Proposing a novel deep network for detecting COVID-19 based on chest images, Learning without forgetting by leveraging transfer learning for detecting COVID-19 infection from CT images, Classification and visual explanation for COVID-19 pneumonia from CT images using triple learning, ANFIS-Net for automatic detection of COVID-19, https://doi.org/10.1007/s13042-022-01676-7, https://www.kesci.com/mw/dataset/5e746ec998d4a8002d2b0861, https://doi.org/10.1007/s12559-020-09775-9, http://creativecommons.org/licenses/by/4.0/. This is one of the reasons for the high computational efficiency of the algorithm. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition1251–1258 (2017). 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. As seen in Table 3, on Dataset 1, the FO-MPA outperformed the other algorithms in the mean of fitness value as it achieved the smallest average fitness function value followed by SMA, HHO, HGSO, SCA, BGWO, MPA, and BPSO, respectively whereas, the SGA and WOA showed the worst results. In Future of Information and Communication Conference, 604–620 (Springer, 2020). Eng. ), such as \(5\times 5\), \(3 \times 3\), \(1 \times 1\). Our proposed approach is called Inception Fractional-order Marine Predators Algorithm (IFM), where we combine Inception (I) with Fractional-order Marine Predators Algorithm (FO-MPA). Signal Process. Detection of lung cancer on chest ct images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. In this paper, we used two different datasets. arXiv:2003.11597 (2020). & Unlersen, M. F. Covid-19 diagnosis using state-of-the-art cnn architecture features and bayesian optimization. The experimental results show that the RMT-Net model has a Test_ acc of 97.65% on the X-ray image dataset, 99.12% on the CT image dataset, which both higher than the other four models. Brain tumor segmentation with deep neural networks. 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. Havaei, M. et al. The Transformer encoder consists of two modules, Multi-head Self-Attention(MHSA) and Multilayer Perceptron (MLP). Control 73, 103371 (2022). Int. For example, Lambin et al.7 proposed an efficient approach called Radiomics to extract medical image features. They applied a fuzzy decision tree classifier, and they found that fuzzy PSO improved the classification accuracy. The accuracy measure is used in the classification phase. It also contributes to minimizing resource consumption which consequently, reduces the processing time. For this motivation, we utilize the FC concept with the MPA algorithm to boost the second step of the standard version of the algorithm. 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. 69, 46–61 (2014). 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. The accuracy rate was 98% on three open source CT scan datasets. Biol. In terms of model classification performance, the RMT-Net model has higher specificity, sensitivity and accuracy. (33)), showed that FO-MPA also achieved the best value of the fitness function compared to others. 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. Therefore, several pre-trained models have won many international image classification competitions such as VGGNet24, Resnet25, Nasnet26, Mobilenet27, Inception28 and Xception29. An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm. The resulting raster from image … where CF is the parameter that controls the step size of movement for the predator. Acharya, U. R. et al. Bukhari, S. U. K., Bukhari, S. S. K., Syed, A. The MLP contains the GELU activation function and two fully connected layers. Okolo, G. I., Katsigiannis, S. & Ramzan, N. Ievit: An enhanced vision transformer architecture for chest x-ray image classification. Gu, Q., Zhu, L. & Cai, Z. I. S. of Medical Radiology. Compared with CNN, VIT can pay more attention to global features and quickly extract features that are beneficial to the network in the early stage. Biol. 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. For instance,\(1\times 1\) conv. supplemented the experiments needed in the paper. Med. College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin, 300222, China, Keying Ren, Geng Hong, Xiaoyan Chen & Zichen Wang, You can also search for this author in 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. 11, 3013 (2022). Radiomics: extracting more information from medical images using advanced feature analysis. COVID-19 So, based on this motivation, we apply MPA as a feature selector from deep features that produced from CNN (largely redundant), which, accordingly minimize capacity and resources consumption and can improve the classification of COVID-19 X-ray images. Efficient classification of white blood cell leukemia with improved swarm optimization of deep features. Lambin, P. et al. 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/]. Taking into consideration the current spread of COVID-19, we believe that these techniques can be applied as a computer-aided tool for diagnosing this virus. For the special case of \(\delta = 1\), the definition of Eq. Coronavirus 2019 (COVID-19) is a new acute respiratory disease that has spread rapidly throughout the world. In CNN, each layer feature with locality, two-dimensional neighborhood structure and shift-invariant. In the current work, the values of k, and \(\zeta\) are set to 2, and 2, respectively. 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. is applied before larger sized kernels are applied to reduce the dimension of the channels, which accordingly, reduces the computation cost. & Baby, C. J. Emphysema medical image classification using fuzzy decision tree with fuzzy particle swarm optimization clustering. For training and … IRBM https://doi.org/10.1016/j.irbm.2019.10.006 (2019). 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. 2 (right). Moreover, from Table 4, 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. Hong, G. et al. Sahlol, A.T., Yousri, D., Ewees, A.A. et al. arXiv preprint arXiv:2003.13145 (2020). Moreover, the Weibull distribution employed to modify the exploration function. Each module adopts residual connection and applies LayerNorm (LN) for normalization. Phys. While55 used different CNN structures. Article  This paper proposes a novel deep learning network based on ResNet-50 merged transformer named RMT-Net. In recent years, Vision Transformer has made a breakthrough in the field of computer vision. (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. Apostolopoulos, I. D., Aznaouridis, S. I. IoT-Based Classification and Differential Diagnosis of COVID-19 … Very deep convolutional networks for large-scale image recognition. Rahimzadeh, M., Attar, A. Nature 503, 535–538 (2013). J. A. et al. Dis. Chen, X. et al. 11, 243–258 (2007). Classification of COVID-19 chest X-rays with deep learning: new …

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