Li et al.34 proposed a self-adaptive bat algorithm (BA) to address two problems in lung X-ray images, rebalancing, and feature selection. \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. A features extraction method using the Histogram of Oriented Gradients (HOG) algorithm and the Linear Support Vector Machine (SVM), K-Nearest Neighbor (KNN) Medium and Decision Tree (DT) Coarse Tree classification methods can be used in the diagnosis of Covid-19 disease. Thereafter, the FO-MPA parameters are applied to update the solutions of the current population. Sci. However, it was clear that VGG19 and MobileNet achieved the best performance over other CNNs. The symbol \(R_B\) refers to Brownian motion. and A.A.E. Szegedy, C. et al. In general, MPA is a meta-heuristic technique that simulates the behavior of the prey and predator in nature37. 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 2018 IEEE International Symposium on Circuits and Systems (ISCAS), 15 (IEEE, 2018). Eng. Such methods might play a significant role as a computer-aided tool for image-based clinical diagnosis soon. Li, J. et al. Scientific Reports (Sci Rep) A.T.S. MathSciNet Use of chest ct in combination with negative rt-pcr assay for the 2019 novel coronavirus but high clinical suspicion. COVID-19 (coronavirus disease 2019) is a new viral infection disease that is widely spread worldwide. Slider with three articles shown per slide. 2 (left). AMERICAN JOURNAL OF EMERGENCY MEDICINE COVID-19: Facemask use prevalence in international airports in Asia, Europe and the Americas, March 2020 arXiv preprint arXiv:1711.05225 (2017). Lambin, P. et al. Eq. Sahlol, A. T., Kollmannsberger, P. & Ewees, A. 9, 674 (2020). 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. Image segmentation is a necessary image processing task that applied to discriminate region of interests (ROIs) from the area of outsides. Table3 shows the numerical results of the feature selection phase for both datasets. a cough chills difficulty breathing tiredness body aches headaches a new loss of taste or smell a sore throat nausea and vomiting diarrhea Not everyone with COVID-19 develops all of these. where r is the run numbers. J. wrote the intro, related works and prepare results. Computational image analysis techniques play a vital role in disease treatment and diagnosis. Int. 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. To evaluate the performance of the proposed model, we computed the average of both best values and the worst values (Max) as well as STD and computational time for selecting features. Contribute to hellorp1990/Covid-19-USF development by creating an account on GitHub. Also, As seen in Fig. Inception architecture is described in Fig. Mobilenets: Efficient convolutional neural networks for mobile vision applications. Continuing on my commitment to share small but interesting things in Google Cloud, this time I created a model for a & Cao, J. First: prey motion based on FC the motion of the prey of Eq. Math. The evaluation showed that the RDFS improved SVM robustness against reconstruction kernel and slice thickness. org (2015). Figure5, shows that FO-MPA shows an efficient and faster convergence than the other optimization algorithms on both datasets. Google Scholar. 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. I am passionate about leveraging the power of data to solve real-world problems. Besides, all algorithms showed the same statistical stability in STD measure, except for HHO and HGSO. Syst. Syst. The HGSO also was ranked last. Multimedia Tools Appl. New Images of Novel Coronavirus SARS-CoV-2 Now Available NIAID Now | February 13, 2020 This scanning electron microscope image shows SARS-CoV-2 (yellow)also known as 2019-nCoV, the virus that causes COVID-19isolated from a patient in the U.S., emerging from the surface of cells (blue/pink) cultured in the lab. 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. Image Anal. Chollet, F. Keras, a python deep learning library. Whereas, the slowest and the insufficient convergences were reported by both SGA and WOA in Dataset 1 and by SGA in Dataset 2. In Smart Intelligent Computing and Applications, 305313 (Springer, 2019). To survey the hypothesis accuracy of the models. 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. 11314, 113142S (International Society for Optics and Photonics, 2020). If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. et al. and JavaScript. Cite this article. It based on using a deep convolutional neural network (Inception) for extracting features from COVID-19 images, then filtering the resulting features using Marine Predators Algorithm (MPA), enhanced by fractional-order calculus(FO). Two real datasets about COVID-19 patients are studied in this paper. \(\Gamma (t)\) indicates gamma function. \(\bigotimes\) indicates the process of element-wise multiplications. 97, 849872 (2019). Therefore, in this paper, we propose a hybrid classification approach of COVID-19. Accordingly, the prey position is upgraded based the following equations. In this paper, we try to integrate deep transfer-learning-based methods, along with a convolutional block attention module (CBAM), to focus on the relevant portion of the feature maps to conduct an image-based classification of human monkeypox disease. Article Besides, the binary classification between two classes of COVID-19 and normal chest X-ray is proposed. arXiv preprint arXiv:1409.1556 (2014). 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. Thank you for visiting nature.com. Dual feature selection and rebalancing strategy using metaheuristic optimization algorithms in x-ray image datasets. Book 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. Comput. 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. As seen in Fig. Al-qaness, M. A., Ewees, A. Whereas, the worst algorithm was BPSO. IEEE Trans. The results are the best achieved compared to other CNN architectures and all published works in the same datasets. 95, 5167 (2016). While no feature selection was applied to select best features or to reduce model complexity. One of the drawbacks of pre-trained models, such as Inception, is that its architecture required large memory requirements as well as storage capacity (92 M.B), which makes deployment exhausting and a tiresome task. COVID-19 image classification using deep features and fractional-order marine predators algorithm. For the special case of \(\delta = 1\), the definition of Eq. Moreover, we design a weighted supervised loss that assigns higher weight for . FCM reinforces the ANFIS classification learning phase based on the features of COVID-19 patients. In54, AlexNet pre-trained network was used to extract deep features then applied PCA to select the best features by eliminating highly correlated features. 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. Huang, P. et al. In such a case, in order to get the advantage of the power of CNN and also, transfer learning can be applied to minimize the computational costs21,22. Sahlol, A.T., Yousri, D., Ewees, A.A. et al. They applied the SVM classifier with and without RDFS. In Iberian Conference on Pattern Recognition and Image Analysis, 176183 (Springer, 2011). Our results indicate that the VGG16 method outperforms . Objective: Lung image classification-assisted diagnosis has a large application market. 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 . Netw. Blog, G. Automl for large scale image classification and object detection. SharifRazavian, A., Azizpour, H., Sullivan, J. 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. 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. Scientific Reports Volume 10, Issue 1, Pages - Publisher. 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. Knowl. Mirjalili, S., Mirjalili, S. M. & Lewis, A. Grey wolf optimizer. & Baby, C.J. Emphysema medical image classification using fuzzy decision tree with fuzzy particle swarm optimization clustering. Google Scholar. For both datasets, the Covid19 images were collected from patients with ages ranging from 40-84 from both genders. Can ai help in screening viral and covid-19 pneumonia? Currently, a new coronavirus, called COVID-19, has spread to many countries, with over two million infected people or so-called confirmed cases. The two datasets consist of X-ray COVID-19 images by international Cardiothoracic radiologist, researchers and others published on Kaggle. 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. Provided by the Springer Nature SharedIt content-sharing initiative, Environmental Science and Pollution Research (2023), Archives of Computational Methods in Engineering (2023), Arabian Journal for Science and Engineering (2023). The memory terms of the prey are updated at the end of each iteration based on first in first out concept. A. We can call this Task 2. The algorithm combines the assessment of image quality, digital image processing and deep learning for segmentation of the lung tissues and their classification. We are hiring! The accuracy measure is used in the classification phase. Toaar, M., Ergen, B. Dhanachandra, N. & Chanu, Y. J. and pool layers, three fully connected layers, the last one performs classification. Biol. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray 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. Vis. Radiomics: extracting more information from medical images using advanced feature analysis. Support Syst. Li, S., Chen, H., Wang, M., Heidari, A. From Fig. (14)-(15) are implemented in the first half of the agents that represent the exploitation. The prey follows Weibull distribution during discovering the search space to detect potential locations of its food. A properly trained CNN requires a lot of data and CPU/GPU time. 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. 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. On the second dataset, dataset 2 (Fig. 111, 300323. Correspondence to Multimedia Tools Appl. 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. In this paper, after applying Chi-square, the feature vector is minimized for both datasets from 51200 to 2000. It can be concluded that FS methods have proven their advantages in different medical imaging applications19. Med. }\delta (1-\delta )(2-\delta ) U_{i}(t-2)\\&\quad + \frac{1}{4! all above stages are repeated until the termination criteria is satisfied. is applied before larger sized kernels are applied to reduce the dimension of the channels, which accordingly, reduces the computation cost. After feature extraction, we applied FO-MPA to select the most significant features. Very deep convolutional networks for large-scale image recognition. Inspired by this concept, Faramarzi et al.37 developed the MPA algorithm by considering both of a predator a prey as solutions. By submitting a comment you agree to abide by our Terms and Community Guidelines. Artif. In this paper, we used TPUs for powerful computation, which is more appropriate for CNN. Future Gener. Propose similarity regularization for improving C. Figure7 shows the most recent published works as in54,55,56,57 and44 on both dataset 1 and dataset 2. In this experiment, the selected features by FO-MPA were classified using KNN. Also, in12, an Fs method based on SVM was proposed to detect Alzheimers disease from SPECT images. You have a passion for computer science and you are driven to make a difference in the research community? Generally, the most stable algorithms On dataset 1 are WOA, SCA, HGSO, FO-MPA, and SGA, respectively. 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). The family of coronaviruses is considered serious pathogens for people because they infect respiratory, hepatic, gastrointestinal, and neurologic diseases. & Zhu, Y. Kernel feature selection to fuse multi-spectral mri images for brain tumor segmentation. In Eq. 11, 243258 (2007). The evaluation confirmed that FPA based FS enhanced classification accuracy. In the current work, the values of k, and \(\zeta\) are set to 2, and 2, respectively. Health Inf. The \(\delta\) symbol refers to the derivative order coefficient. D.Y. By filtering titles, abstracts, and content in the Google Scholar database, this literature review was able to find 19 related papers to answer two research questions, i.e. 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. ADS Eurosurveillance 18, 20503 (2013). All authors discussed the results and wrote the manuscript together. (8) can be remodeled as below: where \(D^1[x(t)]\) represents the difference between the two followed events. Article Memory FC prospective concept (left) and weibull distribution (right). Phys. J. Clin. 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 . (22) can be written as follows: By using the discrete form of GL definition of Eq. A survey on deep learning in medical image analysis. Automated detection of covid-19 cases using deep neural networks with x-ray images. https://keras.io (2015). implemented the deep neural networks and classification as well as prepared the related figures and manuscript text. Google Scholar. Sci Rep 10, 15364 (2020). 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. 42, 6088 (2017). Recently, a combination between the fractional calculus tool and the meta-heuristics opens new doors in providing robust and reliable variants41. 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. Arijit Dey, Soham Chattopadhyay, Ram Sarkar, Dandi Yang, Cristhian Martinez, Jesus Carretero, Jess Alejandro Alzate-Grisales, Alejandro Mora-Rubio, Reinel Tabares-Soto, Lo Dumortier, Florent Gupin, Thomas Grenier, Linda Wang, Zhong Qiu Lin & Alexander Wong, Afnan Al-ali, Omar Elharrouss, Somaya Al-Maaddeed, Robbie Sadre, Baskaran Sundaram, Daniela Ushizima, Zahid Ullah, Muhammad Usman, Jeonghwan Gwak, Scientific Reports Softw. J. Med. The symbol \(r\in [0,1]\) represents a random number. Chong, D. Y. et al. 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. 43, 302 (2019). & 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. Li et al.36 proposed an FS method using a discrete artificial bee colony (ABC) to improve the classification of Parkinsons disease. Jcs: An explainable covid-19 diagnosis system by joint classification and segmentation. Table2 shows some samples from two datasets. & Carlsson, S. Cnn features off-the-shelf: an astounding baseline for recognition. Ozturk et al. Stage 2: The prey/predator in this stage begin exploiting the best location that detects for their foods. They used K-Nearest Neighbor (kNN) to classify x-ray images collected from Montgomery dataset, and it showed good performances. The next process is to compute the performance of each solution using fitness value and determine which one is the best solution. Remainder sections are organized as follows: Material and methods sectionpresents the methodology and the techniques used in this work including model structure and description. 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. So, there might be sometimes some conflict issues regarding the features vector file types or issues related to storage capacity and file transferring. Biomed. 121, 103792 (2020). They also used the SVM to classify lung CT images. Figure6 shows a comparison between our FO-MPA approach and other CNN architectures. 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). Abbas, A., Abdelsamea, M.M. & Gaber, M.M. Classification of covid-19 in chest x-ray images using detrac deep convolutional neural network. However, the proposed FO-MPA approach has an advantage in performance compared to other works. Layers are applied to extract different types of features such as edges, texture, colors, and high-lighted patterns from the images. Therefore in MPA, for the first third of the total iterations, i.e., \(\frac{1}{3}t_{max}\)). Average of the consuming time and the number of selected features in both datasets. COVID-19 image classification using deep features and fractional-order marine predators algorithm, $$\begin{aligned} \chi ^2=\sum _{k=1}^{n} \frac{(O_k - E_k)^2}{E_k} \end{aligned}$$, $$\begin{aligned} ni_{j}=w_{j}C_{j}-w_{left(j)}C_{left(j)}-w_{right(j)}C_{right(j)} \end{aligned}$$, $$\begin{aligned} fi_{i}=\frac{\sum _{j:node \mathbf \ {j} \ splits \ on \ feature \ i}ni_{j}}{\sum _{{k}\in all \ nodes }ni_{k}} \end{aligned}$$, $$\begin{aligned} normfi_{i}=\frac{fi_{i}}{\sum _{{j}\in all \ nodes }fi_{j}} \end{aligned}$$, $$\begin{aligned} REfi_{i}=\frac{\sum _{j \in all trees} normfi_{ij}}{T} \end{aligned}$$, $$\begin{aligned} D^{\delta }(U(t))=\lim \limits _{h \rightarrow 0} \frac{1}{h^\delta } \sum _{k=0}^{\infty }(-1)^{k} \begin{pmatrix} \delta \\ k\end{pmatrix} U(t-kh), \end{aligned}$$, $$\begin{aligned} \begin{pmatrix} \delta \\ k \end{pmatrix}= \frac{\Gamma (\delta +1)}{\Gamma (k+1)\Gamma (\delta -k+1)}= \frac{\delta (\delta -1)(\delta -2)\ldots (\delta -k+1)}{k! This dataset consists of 219 COVID-19 positive images and 1341 negative COVID-19 images. PubMedGoogle Scholar. Podlubny, I. Nguyen, L.D., Lin, D., Lin, Z. 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). Inf. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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. Johnson, D.S., Johnson, D. L.L., Elavarasan, P. & Karunanithi, A. 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 combination of Conv. arXiv preprint arXiv:2003.13145 (2020). Also, they require a lot of computational resources (memory & storage) for building & training. 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. The predator tries to catch the prey while the prey exploits the locations of its food. & Mirjalili, S. Slime mould algorithm: A new method for stochastic optimization. The GL in the discrete-time form can be modeled as below: where T is the sampling period, and m is the length of the memory terms (memory window). Eng. where \(R\in [0,1]\) is a random vector drawn from a uniform distribution and \(P=0.5\) is a constant number. It is obvious that such a combination between deep features and a feature selection algorithm can be efficient in several image classification tasks. It classifies the chest X-ray images into three categories that includes Covid-19, Pneumonia and normal. The Shearlet transform FS method showed better performances compared to several FS methods. 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). where \(R_L\) has random numbers that follow Lvy distribution. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 19 (2015). PubMed Central (18)(19) for the second half (predator) as represented below. The results show that, using only 6 epochs for training, the CNNs achieved very high performance on the classification task. Computer Department, Damietta University, Damietta, Egypt, Electrical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum, Egypt, State Key Laboratory for Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China, Department of Applied Informatics, Vytautas Magnus University, Kaunas, Lithuania, Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt, School of Computer Science and Robotics, Tomsk Polytechnic University, Tomsk, Russia, You can also search for this author in Decaf: A deep convolutional activation feature for generic visual recognition. 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. CAS Using X-ray images we can train a machine learning classifier to detect COVID-19 using Keras and TensorFlow. (9) as follows. }\delta (1-\delta ) U_{i}(t-1)+ \frac{1}{3! To further analyze the proposed algorithm, we evaluate the selected features by FO-MPA by performing classification. CAS Hence, it was discovered that the VGG-16 based DTL model classified COVID-19 better than the VGG-19 based DTL model. While55 used different CNN structures. CNNs are more appropriate for large datasets. However, the modern name is tenggiling.In Javanese it is terenggiling; and in the Philippine languages, it is goling, tanggiling, or balintong (with the same meaning).. Med. Some people say that the virus of COVID-19 is. The updating operation repeated until reaching the stop condition. 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. Med. The test accuracy obtained for the model was 98%. 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). 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. Purpose The study aimed at developing an AI . 2022 May;144:105350. doi: 10.1016/j.compbiomed.2022.105350. While the second dataset, dataset 2 was collected by a team of researchers from Qatar University in Qatar and the University of Dhaka in Bangladesh along with collaborators from Pakistan and Malaysia medical doctors44. 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. Sign up for the Nature Briefing newsletter what matters in science, free to your inbox daily. A. et al. Harris hawks optimization: algorithm and applications. The conference was held virtually due to the COVID-19 pandemic. 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.