In general, MPA is a meta-heuristic technique that simulates the behavior of the prey and predator in nature37. In Future of Information and Communication Conference, 604620 (Springer, 2020). The proposed approach selected successfully 130 and 86 out of 51 K features extracted by inception from dataset 1 and dataset 2, while improving classification accuracy at the same time. Cauchemez, S. et al. 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. Improving the ranking quality of medical image retrieval using a genetic feature selection method. Get the most important science stories of the day, free in your inbox. https://keras.io (2015). In this paper, each feature selection algorithm were exposed to select the produced feature vector from Inception aiming at selecting only the most relevant features. https://doi.org/10.1155/2018/3052852 (2018). Google Scholar. Lambin, P. et al. Radiology 295, 2223 (2020). This combination should achieve two main targets; high performance and resource consumption, storage capacity which consequently minimize processing time. Whereas, FO-MPA, MPA, HGSO, and WOA showed similar STD results. (iii) To implement machine learning classifiers for classification of COVID and non-COVID image classes. 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.. It also contributes to minimizing resource consumption which consequently, reduces the processing time. Also, WOA algorithm showed good results in all measures, unlike dataset 1, which can conclude that no algorithm can solve all kinds of problems. Litjens, G. et al. It also shows that FO-MPA can select the smallest subset of features, which reflects positively on performance. 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. It is obvious that such a combination between deep features and a feature selection algorithm can be efficient in several image classification tasks. 115, 256269 (2011). 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. In the current work, the values of k, and \(\zeta\) are set to 2, and 2, respectively. <span> <h5>Background</h5> <p>The COVID19 pandemic has precipitated global apprehensions about increased fatalities and raised concerns about gaps in healthcare . Image segmentation is a necessary image processing task that applied to discriminate region of interests (ROIs) from the area of outsides. The memory properties of Fc calculus makes it applicable to the fields that required non-locality and memory effect. ADS Based on Standard Deviation measure (STD), the most stable algorithms were SCA, SGA, BPSO, and bGWO, respectively. They also used the SVM to classify lung CT images. In this paper, Inception is applied as a feature extractor, where the input image shape is (229, 229, 3). 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. You are using a browser version with limited support for CSS. Corona Virus lung infected X-Ray Images accessible by Kaggle a complete 590 images, which classified in 2 classes: typical and Covid-19. contributed to preparing results and the final figures. Computed tomography (CT) and magnetic resonance imaging (MRI) represent valuable input to AI algorithms, scanning human body sections for the sake of diagnosis. Support Syst. M.A.E. Moreover, other COVID-19 positive images were added by the Italian Society of Medical and Interventional Radiology (SIRM) COVID-19 Database45. As Inception examines all X-ray images over and over again in each epoch during the training, these rapid ups and downs are slowly minimized in the later part of the training. Using X-ray images we can train a machine learning classifier to detect COVID-19 using Keras and TensorFlow. 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. 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. J. Clin. Biomed. A.T.S. 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. PubMed All classication models ever, the virus mutates, and new variants emerge and dis- performed better in classifying the Non-COVID-19 images appear. Imaging Syst. Fractional Differential Equations: An Introduction to Fractional Derivatives, Fdifferential Equations, to Methods of their Solution and Some of Their Applications Vol. 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. Podlubny, I. Access through your institution. arXiv preprint arXiv:2004.05717 (2020). In Iberian Conference on Pattern Recognition and Image Analysis, 176183 (Springer, 2011). This dataset consists of 219 COVID-19 positive images and 1341 negative COVID-19 images. Average of the consuming time and the number of selected features in both datasets. J. Med. Narayanan, S.J., Soundrapandiyan, R., Perumal, B. Johnson et al.31 applied the flower pollination algorithm (FPA) to select features from CT images of the lung, to detect lung cancers. Netw. Mirjalili, S., Mirjalili, S. M. & Lewis, A. Grey wolf optimizer. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition12511258 (2017). Int. Bibliographic details on CECT: Controllable Ensemble CNN and Transformer for COVID-19 image classification by capturing both local and global image features. }\delta (1-\delta ) U_{i}(t-1)+ \frac{1}{3! The . (22) can be written as follows: By taking into account the early mentioned relation in Eq. In the meantime, to ensure continued support, we are displaying the site without styles Highlights COVID-19 CT classification using chest tomography (CT) images. Detection of lung cancer on chest ct images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. Faramarzi et al.37 divided the agents for two halves and formulated Eqs. It can be concluded that FS methods have proven their advantages in different medical imaging applications19. Stage 1: After the initialization, the exploration phase is implemented to discover the search space. (4). Four measures for the proposed method and the compared algorithms are listed. Li et al.34 proposed a self-adaptive bat algorithm (BA) to address two problems in lung X-ray images, rebalancing, and feature selection. Lilang Zheng, Jiaxuan Fang, Xiaorun Tang, Hanzhang Li, Jiaxin Fan, Tianyi Wang, Rui Zhou, Zhaoyan Yan: PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer. arXiv preprint arXiv:1711.05225 (2017). I am passionate about leveraging the power of data to solve real-world problems. Automated detection of alzheimers disease using brain mri imagesa study with various feature extraction techniques. A combination of fractional-order and marine predators algorithm (FO-MPA) is considered an integration among a robust tool in mathematics named fractional-order calculus (FO). (5). Medical imaging techniques are very important for diagnosing diseases. A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models. (8) at \(T = 1\), the expression of Eq. Evaluation outcomes showed that GA based FS methods outperformed traditional approaches, such as filter based FS and traditional wrapper methods. Huang, P. et al. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 19 (2015). Bisong, E. Building Machine Learning and Deep Learning Models on Google Cloud Platform (Springer, Berlin, 2019). A.A.E. (1): where \(O_k\) and \(E_k\) refer to the actual and the expected feature value, respectively. arXiv preprint arXiv:2003.13815 (2020). IEEE Trans. Future Gener. 4a, the SMA was considered as the fastest algorithm among all algorithms followed by BPSO, FO-MPA, and HHO, respectively, while MPA was the slowest algorithm. Inspired by this concept, Faramarzi et al.37 developed the MPA algorithm by considering both of a predator a prey as solutions. However, it has some limitations that affect its quality. \(\bigotimes\) indicates the process of element-wise multiplications. Eng. The data was collected mainly from retrospective cohorts of pediatric patients from Guangzhou Women and Childrens medical center. The optimum path forest (OPF) classifier was applied to classify pulmonary nodules based on CT images. For each decision tree, node importance is calculated using Gini importance, Eq. In this experiment, the selected features by FO-MPA were classified using KNN. In Dataset 2, FO-MPA also is reported as the highest classification accuracy with the best and mean measures followed by the BPSO. They used K-Nearest Neighbor (kNN) to classify x-ray images collected from Montgomery dataset, and it showed good performances. CAS Comput. Coronavirus Disease (COVID-19): A primer for emergency physicians (2020) Summer Chavez et al. Nevertheless, a common mistake in COVID-19 dataset fusion, mainly on classification tasks, is that by mixing many datasets of COVID-19 and using as Control images another dataset, there will be . Simonyan, K. & Zisserman, A. Article Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. Generally, the most stable algorithms On dataset 1 are WOA, SCA, HGSO, FO-MPA, and SGA, respectively. To further analyze the proposed algorithm, we evaluate the selected features by FO-MPA by performing classification. Comput. Li et al.36 proposed an FS method using a discrete artificial bee colony (ABC) to improve the classification of Parkinsons disease. Figure5 illustrates the convergence curves for FO-MPA and other algorithms in both datasets. Sohail, A. S.M., Bhattacharya, P., Mudur, S.P. & Krishnamurthy, S. Classification of ultrasound medical images using distance based feature selection and fuzzy-svm. There are three main parameters for pooling, Filter size, Stride, and Max pool. Use of chest ct in combination with negative rt-pcr assay for the 2019 novel coronavirus but high clinical suspicion. However, the proposed IMF approach achieved the best results among the compared algorithms in least time. Authors Stage 2 has been executed in the second third of the total number of iterations when \(\frac{1}{3}t_{max}< t< \frac{2}{3}t_{max}\). In our experiment, we randomly split the data into 70%, 10%, and 20% for training, validation, and testing sets, respectively. A. 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]. Eurosurveillance 18, 20503 (2013). He, K., Zhang, X., Ren, S. & Sun, J. Afzali, A., Mofrad, F.B. Eng. 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Howard, A.G. etal. The prey follows Weibull distribution during discovering the search space to detect potential locations of its food. Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. This stage can be mathematically implemented as below: In Eq. 42, 6088 (2017). Therefore, several pre-trained models have won many international image classification competitions such as VGGNet24, Resnet25, Nasnet26, Mobilenet27, Inception28 and Xception29. 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. In54, AlexNet pre-trained network was used to extract deep features then applied PCA to select the best features by eliminating highly correlated features. Imaging 29, 106119 (2009). So, there might be sometimes some conflict issues regarding the features vector file types or issues related to storage capacity and file transferring. In this paper, we used two different datasets. Comput. and M.A.A.A. (2) calculated two child nodes. 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. Nguyen, L.D., Lin, D., Lin, Z. As seen in Fig. Methods Med. 152, 113377 (2020). Med. 4 and Table4 list these results for all algorithms. Credit: NIAID-RML While no feature selection was applied to select best features or to reduce model complexity. PubMedGoogle Scholar. Civit-Masot et al. 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. 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. (14)-(15) are implemented in the first half of the agents that represent the exploitation. Knowl. On January 20, 2023, Japanese Prime Minister Fumio Kishida announced that the country would be downgrading the COVID-19 classification. For example, Lambin et al.7 proposed an efficient approach called Radiomics to extract medical image features. 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). Blog, G. Automl for large scale image classification and object detection. The lowest accuracy was obtained by HGSO in both measures. Automatic CNN-based Chest X-Ray (CXR) image classification for detecting Covid-19 attracted so much attention. Article Adv. 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. Its structure is designed based on experts' knowledge and real medical process. The results show that, using only 6 epochs for training, the CNNs achieved very high performance on the classification task. Afzali et al.15 proposed an FS method based on principal component analysis and contour-based shape descriptors to detect Tuberculosis from lung X-Ray Images.