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. 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). Cauchemez, S. et al. \(Fit_i\) denotes a fitness function value. New machine learning method for image-based diagnosis of COVID-19 - PLOS 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).. Toaar, M., Ergen, B. 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. The proposed IMF approach is employed to select only relevant and eliminate unnecessary features. Article Deep residual learning for image recognition. 78, 2091320933 (2019). Apostolopoulos, I. D. & Mpesiana, T. A. Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Internet Explorer). Stage 1: After the initialization, the exploration phase is implemented to discover the search space. It is obvious that such a combination between deep features and a feature selection algorithm can be efficient in several image classification tasks. 51, 810820 (2011). 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. Also, As seen in Fig. Propose a novel robust optimizer called Fractional-order Marine Predators Algorithm (FO-MPA) to select efficiently the huge feature vector produced from the CNN. A survey on deep learning in medical image analysis. 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. (8) at \(T = 1\), the expression of Eq. 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. An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm. It is calculated between each feature for all classes, as in Eq. In this paper, we used two different datasets. COVID 19 X-ray image classification. 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 . Softw. & 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. 97, 849872 (2019). Phys. In general, feature selection (FS) methods are widely employed in various applications of medical imaging applications. 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. COVID-19 Detection via Image Classification using Deep Learning on 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. A.T.S. However, it has some limitations that affect its quality. Narayanan, S.J., Soundrapandiyan, R., Perumal, B. Google Scholar. With the help of numerous algorithms in AI, modern COVID-19 cases can be detected and managed in a classified framework. Abadi, M. et al. Chollet, F. Xception: Deep learning with depthwise separable convolutions. This dataset consists of 219 COVID-19 positive images and 1341 negative COVID-19 images. Appl. The second CNN architecture classifies the X-ray image into three classes, i.e., normal, pneumonia, and COVID-19. It also contributes to minimizing resource consumption which consequently, reduces the processing time. 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 . Isolation and characterization of a bat sars-like coronavirus that uses the ace2 receptor. The model was developed using Keras library47 with Tensorflow backend48. Boosting COVID-19 Image Classification Using MobileNetV3 and Aquila 10, 10331039 (2020). If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. In Dataset 2, FO-MPA also is reported as the highest classification accuracy with the best and mean measures followed by the BPSO. Image Anal. Detecting COVID-19 in X-ray images with Keras - PyImageSearch Hence, the FC memory is applied during updating the prey locating in the second step of the algorithm to enhance the exploitation stage. 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). where CF is the parameter that controls the step size of movement for the predator. A Review of Deep Learning Imaging Diagnostic Methods for COVID-19 The test accuracy obtained for the model was 98%. COVID-19 Chest X -Ray Image Classification with Neural Network Currently we are suffering from COVID-19, and the situation is very serious. This algorithm is tested over a global optimization problem. Accordingly, the prey position is upgraded based the following equations. 41, 923 (2019). MRFGRO: a hybrid meta-heuristic feature selection method for screening COVID-19 using deep features, Detection and analysis of COVID-19 in medical images using deep learning techniques, Cov-caldas: A new COVID-19 chest X-Ray dataset from state of Caldas-Colombia, Deep learning in veterinary medicine, an approach based on CNN to detect pulmonary abnormalities from lateral thoracic radiographs in cats, COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images, ANFIS-Net for automatic detection of COVID-19, A multi-scale gated multi-head attention depthwise separable CNN model for recognizing COVID-19, Validating deep learning inference during chest X-ray classification for COVID-19 screening, Densely attention mechanism based network for COVID-19 detection in chest X-rays, https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/, https://github.com/ieee8023/covid-chestxray-dataset, https://stanfordmlgroup.github.io/projects/chexnet, https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia, https://www.sirm.org/en/category/articles/covid-19-database/, https://drive.google.com/file/d/1-oK-eeEgdCMCnykH364IkAK3opmqa9Rvasx/view?usp=sharing, https://doi.org/10.1016/j.irbm.2019.10.006, https://research.googleblog.com/2017/11/automl-for-large-scaleimage.html, https://doi.org/10.1016/j.engappai.2020.103662, https://www.sirm.org/category/senza-categoria/covid-19/, https://doi.org/10.1016/j.future.2020.03.055, http://creativecommons.org/licenses/by/4.0/, Skin cancer detection using ensemble of machine learning and deep learning techniques, Plastic pollution induced by the COVID-19: Environmental challenges and outlook, An Inclusive Survey on Marine Predators Algorithm: Variants andApplications, A Multi-strategy Improved Outpost and Differential Evolution Mutation Marine Predators Algorithm for Global Optimization, A light-weight convolutional Neural Network Architecture for classification of COVID-19 chest X-Ray images. 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. Therefore, in this paper, we propose a hybrid classification approach of COVID-19. used a dark Covid-19 network for multiple classification experiments on Covid-19 with an accuracy of 87% [ 23 ]. One of the best methods of detecting. The combination of Conv. In this work, we have used four transfer learning models, VGG16, InceptionV3, ResNet50, and DenseNet121 for the classification tasks. 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. Also, it has killed more than 376,000 (up to 2 June 2020) [Coronavirus disease (COVID-2019) situation reports: (https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/)]. 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 . Initialize solutions for the prey and predator. The COVID-19 pandemic has been having a severe and catastrophic effect on humankind and is being considered the most crucial health calamity of the century. Objective: Lung image classification-assisted diagnosis has a large application market. (22) can be written as follows: By using the discrete form of GL definition of Eq. Vis. [PDF] Detection and Severity Classification of COVID-19 in CT Images They are distributed among people, bats, mice, birds, livestock, and other animals1,2. implemented the FO-MPA swarm optimization and prepared the related figures and manuscript text. (18)(19) for the second half (predator) as represented below. Math. 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. and pool layers, three fully connected layers, the last one performs classification. 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. Fung, G. & Stoeckel, J. Svm feature selection for classification of spect images of alzheimers disease using spatial information. Semi-supervised Learning for COVID-19 Image Classification via ResNet contributed to preparing results and the final figures. Cancer 48, 441446 (2012). 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. 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. Recently, a combination between the fractional calculus tool and the meta-heuristics opens new doors in providing robust and reliable variants41. Article 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). \(\Gamma (t)\) indicates gamma function. In this work, the MPA is enhanced by fractional calculus memory feature, as a result, Fractional-order Marine Predators Algorithm (FO-MPA) is introduced. Google Scholar. We are hiring! COVID-19 Image Classification Using VGG-16 & CNN based on CT - IJRASET For example, Da Silva et al.30 used the genetic algorithm (GA) to develop feature selection methods for ranking the quality of medical images. Cohen, J.P., Morrison, P. & Dao, L. Covid-19 image data collection. 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. 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. In Eq. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. 111, 300323. While the second half of the agents perform the following equations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770778 (2016). FC provides a clear interpretation of the memory and hereditary features of the process. 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). MathSciNet 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. You have a passion for computer science and you are driven to make a difference in the research community? On the second dataset, dataset 2 (Fig. Eng. They shared some parameters, such as the total number of iterations and the number of agents which were set to 20 and 15, respectively. The proposed IMF approach successfully achieves two important targets, selecting small feature numbers with high accuracy. Deep learning models-based CT-scan image classification for automated Automated Quantification of Pneumonia Infected Volume in Lung CT Images Zhang, N., Ruan, S., Lebonvallet, S., Liao, Q. Comput. 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. An efficient feature generation approach based on deep learning and feature selection techniques for traffic classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 19 (2015). Although outbreaks of SARS and MERS had confirmed human to human transmission3, they had not the same spread speed and infection power of the new coronavirus (COVID-19). 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 Technol. Book 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. Dhanachandra and Chanu35 proposed a hybrid method of dynamic PSO and fuzzy c-means to segment two types of medical images, MRI and synthetic images. }, \end{aligned}$$, $$\begin{aligned} D^{\delta }[U(t)]=\frac{1}{T^\delta }\sum _{k=0}^{m} \frac{(-1)^k\Gamma (\delta +1)U(t-kT)}{\Gamma (k+1)\Gamma (\delta -k+1)} \end{aligned}$$, $$\begin{aligned} D^1[U(t)]=U(t+1)-U(t) \end{aligned}$$, $$\begin{aligned} U=Lower+rand_1\times (Upper - Lower ) \end{aligned}$$, $$\begin{aligned} Elite=\left[ \begin{array}{cccc} U_{11}^1&{}U_{12}^1&{}\ldots &{}U_{1d}^1\\ U_{21}^1&{}U_{22}^1&{}\ldots &{}U_{2d}^1\\ \ldots &{}\ldots &{}\ldots &{}\ldots \\ U_{n1}^1&{}U_{n2}^1&{}\ldots &{}U_{nd}^1\\ \end{array}\right] , \, U=\left[ \begin{array}{cccc} U_{11}&{}U_{12}&{}\ldots &{}U_{1d}\\ U_{21}&{}U_{22}&{}\ldots &{}U_{2d}\\ \ldots &{}\ldots &{}\ldots &{}\ldots \\ U_{n1}&{}U_{n2}&{}\ldots &{}U_{nd}\\ \end{array}\right] , \, \end{aligned}$$, $$\begin{aligned} S_i&= {} R_B \bigotimes (Elite_i-R_B\bigotimes U_i), i=1,2,\ldots ,n \end{aligned}$$, $$\begin{aligned} U_i&= {} U_i+P.R\bigotimes S_i \end{aligned}$$, \(\frac{1}{3}t_{max}< t< \frac{2}{3}t_{max}\), $$\begin{aligned} S_i&= {} R_L \bigotimes (Elite_i-R_L\bigotimes U_i), i=1,2,\ldots ,n/2 \end{aligned}$$, $$\begin{aligned} S_i&= {} R_B \bigotimes (R_B \bigotimes Elite_i- U_i), i=1,2,\ldots ,n/2 \end{aligned}$$, $$\begin{aligned} U_i&= {} Elite_i+P.CF\bigotimes S_i,\, CF= \left( 1-\frac{t}{t_{max}} \right) ^{\left(2\frac{t}{t_{max}}\right) } \end{aligned}$$, $$\begin{aligned} S_i&= {} R_L \bigotimes (R_L \bigotimes Elite_i- U_i), i=1,2,\ldots ,n \end{aligned}$$, $$\begin{aligned} U_i&= {} Elite_i+P.CF\bigotimes S_i,\, CF= \left( 1-\frac{t}{t_{max}}\right) ^{\left(2\frac{t}{t_{max}} \right) } \end{aligned}$$, $$\begin{aligned} U_i=\left\{ \begin{array}{ll} U_i+CF [U_{min}+R \bigotimes (U_{max}-U_{min})]\bigotimes W &{} r_5 < FAD \\ U_i+[FAD(1-r)+r](U_{r1}-U_{r2}) &{} r_5 > FAD\\ \end{array}\right. 198 (Elsevier, Amsterdam, 1998). The following stage was to apply Delta variants. Whereas, the worst algorithm was BPSO. Furthermore, deep learning using CNN is considered one of the best choices in medical imaging applications20, especially classification. 2022 May;144:105350. doi: 10.1016/j.compbiomed.2022.105350. \delta U_{i}(t)+ \frac{1}{2! Image segmentation is a necessary image processing task that applied to discriminate region of interests (ROIs) from the area of outsides. Generally, the most stable algorithms On dataset 1 are WOA, SCA, HGSO, FO-MPA, and SGA, respectively. For instance,\(1\times 1\) conv. Brain tumor segmentation with deep neural networks. International Conference on Machine Learning647655 (2014). 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. (5). Acharya, U. R. et al. Four measures for the proposed method and the compared algorithms are listed. Two real datasets about COVID-19 patients are studied in this paper. JMIR Formative Research - Classifying COVID-19 Patients From Chest X-ray Images Using Hybrid Machine Learning Techniques: Development and Evaluation Published on 28.2.2023 in Vol 7 (2023) Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42324, first published August 31, 2022 . 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. Int. arXiv preprint arXiv:2004.07054 (2020). Also, they require a lot of computational resources (memory & storage) for building & training. \(\bigotimes\) indicates the process of element-wise multiplications. Also, because COVID-19 is a virus, distinguish COVID-19 from common viral . Nguyen, L.D., Lin, D., Lin, Z. The parameters of each algorithm are set according to the default values. Marine memory: This is the main feature of the marine predators and it helps in catching the optimal solution very fast and avoid local solutions. Med. 40, 2339 (2020). They concluded that the hybrid method outperformed original fuzzy c-means, and it had less sensitive to noises. Finally, the predator follows the levy flight distribution to exploit its prey location. J. Clin. These datasets contain hundreds of frontal view X-rays and considered the largest public resource for COVID-19 image data. Generally, the proposed FO-MPA approach showed satisfying performance in both the feature selection ratio and the classification rate. Multimedia Tools Appl. For example, Lambin et al.7 proposed an efficient approach called Radiomics to extract medical image features. Expert Syst. A.A.E. Classification of COVID19 using Chest X-ray Images in Keras 4.6 33 ratings Share Offered By In this Guided Project, you will: Learn to Build and Train the Convolutional Neural Network using Keras with Tensorflow as Backend Learn to Visualize Data in Matplotlib Learn to make use of the Trained Model to Predict on a New Set of Data 2 hours The conference was held virtually due to the COVID-19 pandemic.