Early diagnosis, timely treatment, and proper confinement of the infected patients are some possible ways to control the spreading of . Li, H. etal. Cancer 48, 441446 (2012). Appl. The survey asked participants to broadly classify the findings of each chest CT into one of the four RSNA COVID-19 imaging categories, then select which imaging features led to their categorization. Use of chest ct in combination with negative rt-pcr assay for the 2019 novel coronavirus but high clinical suspicion. As seen in Table1, we keep the last concatenation layer which contains the extracted features, so we removed the top layers such as the Flatten, Drop out and the Dense layers which the later performs classification (named as FC layer). Kharrat, A. Scientific Reports Volume 10, Issue 1, Pages - Publisher. Inspired by this concept, Faramarzi et al.37 developed the MPA algorithm by considering both of a predator a prey as solutions. D.Y. 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. 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. Comput. The parameters of each algorithm are set according to the default values. In Eq. This dataset currently contains hundreds of frontal view X-rays and is the largest public resource for COVID-19 image and prognostic data, making it a necessary resource to develop and evaluate tools to aid in the treatment of CO VID-19. Med. However, the proposed FO-MPA approach has an advantage in performance compared to other works. In transfer learning, a CNN which was previously trained on a large & diverse image dataset can be applied to perform a specific classification task by23. Therefore, several pre-trained models have won many international image classification competitions such as VGGNet24, Resnet25, Nasnet26, Mobilenet27, Inception28 and Xception29. Narayanan, S.J., Soundrapandiyan, R., Perumal, B. 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. For fair comparison, each algorithms was performed (run) 25 times to produce statistically stable results.The results are listed in Tables3 and4. It is obvious that such a combination between deep features and a feature selection algorithm can be efficient in several image classification tasks. Classification of COVID-19 X-ray images with Keras and its potential problem | by Yiwen Lai | Analytics Vidhya | Medium Write Sign up 500 Apologies, but something went wrong on our end.. 132, 8198 (2018). Simonyan, K. & Zisserman, A. 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. Thereafter, the FO-MPA parameters are applied to update the solutions of the current population. You have a passion for computer science and you are driven to make a difference in the research community? Article 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. Article 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. 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. Metric learning Metric learning can create a space in which image features within the. 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. I. S. of Medical Radiology. IEEE Trans. Furthermore, using few hundreds of images to build then train Inception is considered challenging because deep neural networks need large images numbers to work efficiently and produce efficient features. We build the first Classification model using VGG16 Transfer leaning framework and second model using Deep Learning Technique Convolutional Neural Network CNN to classify and diagnose the disease and we able to achieve the best accuracy in both the model. Table3 shows the numerical results of the feature selection phase for both datasets. 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. Rep. 10, 111 (2020). Johnson et al.31 applied the flower pollination algorithm (FPA) to select features from CT images of the lung, to detect lung cancers. 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. Chong, D. Y. et al. MathSciNet 111, 300323. Eng. The second CNN architecture classifies the X-ray image into three classes, i.e., normal, pneumonia, and COVID-19. ISSN 2045-2322 (online). The HGSO also was ranked last. The evaluation showed that the RDFS improved SVM robustness against reconstruction kernel and slice thickness. The . arXiv preprint arXiv:2004.07054 (2020). 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. Among the FS methods, the metaheuristic techniques have been established their performance overall other FS methods when applied to classify medical images. (24). 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. 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. 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 . By submitting a comment you agree to abide by our Terms and Community Guidelines. Imaging 35, 144157 (2015). Bibliographic details on CECT: Controllable Ensemble CNN and Transformer for COVID-19 image classification by capturing both local and global image features. This dataset consists of 219 COVID-19 positive images and 1341 negative COVID-19 images. Lett. The lowest accuracy was obtained by HGSO in both measures. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. IEEE Trans. \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. \delta U_{i}(t)+ \frac{1}{2! 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. J. Clin. Key Definitions. 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. While no feature selection was applied to select best features or to reduce model complexity. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770778 (2016). It also contributes to minimizing resource consumption which consequently, reduces the processing time. You are using a browser version with limited support for CSS. Sci. wrote the intro, related works and prepare results. Automated detection of covid-19 cases using deep neural networks with x-ray images. They used different images of lung nodules and breast to evaluate their FS methods. A survey on deep learning in medical image analysis. Hashim, F. A., Houssein, E. H., Mabrouk, M. S., Al-Atabany, W. & Mirjalili, S. Henry gas solubility optimization: a novel physics-based algorithm. Figure5 illustrates the convergence curves for FO-MPA and other algorithms in both datasets. However, some of the extracted features by CNN might not be sufficient, which may affect negatively the quality of the classification images. 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. implemented the FO-MPA swarm optimization and prepared the related figures and manuscript text. In order to normalize the values between 0 and 1 by dividing by the sum of all feature importance values, as in Eq. In my thesis project, I developed an image classification model to detect COVID-19 on chest X-ray medical data using deep learning models such . Springer Science and Business Media LLC Online. Heidari, A. 78, 2091320933 (2019). & Carlsson, S. Cnn features off-the-shelf: an astounding baseline for recognition. Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs. Wish you all a very happy new year ! 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). COVID-19 (coronavirus disease 2019) is a new viral infection disease that is widely spread worldwide. Thank you for visiting nature.com. It achieves a Dice score of 0.9923 for segmentation, and classification accuracies of 0. Purpose The study aimed at developing an AI . https://keras.io (2015). 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. used a dark Covid-19 network for multiple classification experiments on Covid-19 with an accuracy of 87% [ 23 ]. 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. 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. }\delta (1-\delta ) U_{i}(t-1)+ \frac{1}{3! Image Underst. Figure5, shows that FO-MPA shows an efficient and faster convergence than the other optimization algorithms on both datasets. Accordingly, that reflects on efficient usage of memory, and less resource consumption. The two datasets consist of X-ray COVID-19 images by international Cardiothoracic radiologist, researchers and others published on Kaggle. The combination of Conv. My education and internships have equipped me with strong technical skills in Python, deep learning models, machine learning classification, text classification, and more. In 2019 26th National and 4th International Iranian Conference on Biomedical Engineering (ICBME), 194198 (IEEE, 2019). Finally, the sum of the features importance value on each tree is calculated then divided by the total number of trees as in Eq. 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/]. Both the model uses Lungs CT Scan images to classify the covid-19. (15) can be reformulated to meet the special case of GL definition of Eq. The proposed IMF approach is employed to select only relevant and eliminate unnecessary features. 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. 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 a swarm-based feature selection algorithm (Marine Predators Algorithm) to select the most relevant features. Eurosurveillance 18, 20503 (2013). In this work, the MPA is enhanced by fractional calculus memory feature, as a result, Fractional-order Marine Predators Algorithm (FO-MPA) is introduced. 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. J. Med. PubMed IRBM https://doi.org/10.1016/j.irbm.2019.10.006 (2019). They employed partial differential equations for extracting texture features of medical images. Both datasets shared some characteristics regarding the collecting sources. 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 . 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. Therefore in MPA, for the first third of the total iterations, i.e., \(\frac{1}{3}t_{max}\)). Adv. In the current work, the values of k, and \(\zeta\) are set to 2, and 2, respectively. A.A.E. Sahlol, A.T., Yousri, D., Ewees, A.A. et al. Keywords - Journal. Highlights COVID-19 CT classification using chest tomography (CT) images. Isolation and characterization of a bat sars-like coronavirus that uses the ace2 receptor. is applied before larger sized kernels are applied to reduce the dimension of the channels, which accordingly, reduces the computation cost. COVID 19 X-ray image classification. For each decision tree, node importance is calculated using Gini importance, Eq. & Baby, C.J. Emphysema medical image classification using fuzzy decision tree with fuzzy particle swarm optimization clustering. Eq. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. Test the proposed Inception Fractional-order Marine Predators Algorithm (IFM) approach on two publicity available datasets contain a number of positive negative chest X-ray scan images of COVID-19. The Shearlet transform FS method showed better performances compared to several FS methods. The following stage was to apply Delta variants. The algorithm combines the assessment of image quality, digital image processing and deep learning for segmentation of the lung tissues and their classification.
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