covid 19 image classification covid 19 image classification

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. Google Scholar. youngsoul/pyimagesearch-covid19-image-classification - GitHub M.A.E. The proposed cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images, which can reliably localize infections of various shapes and sizes, especially small infection regions, which are rarely considered in recent studies. used VGG16 to classify Covid-19 and achieved good results with an accuracy of 86% [ 22 ]. Covid-19-USF/test.py at master hellorp1990/Covid-19-USF They achieved 98.08 % and 96.51 % of accuracy and F-Score, respectively compared to our approach with 98.77 % and 98.2% for accuracy and F-Score, respectively. In 2018 IEEE International Symposium on Circuits and Systems (ISCAS), 15 (IEEE, 2018). For general case based on the FC definition, the Eq. 2022 May;144:105350. doi: 10.1016/j.compbiomed.2022.105350. 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 . Design incremental data augmentation strategy for COVID-19 CT data. 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. Also, other recent published works39, who combined a CNN architecture with Weighted Symmetric Uncertainty (WSU) to select optimal features for traffic classification. Support Syst. Acharya et al.11 applied different FS methods to classify Alzheimers disease using MRI images. They are distributed among people, bats, mice, birds, livestock, and other animals1,2. Tensorflow: Large-scale machine learning on heterogeneous systems, 2015. 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). Johnson, D.S., Johnson, D. L.L., Elavarasan, P. & Karunanithi, A. Luz, E., Silva, P.L., Silva, R. & Moreira, G. Towards an efficient deep learning model for covid-19 patterns detection in x-ray images. \delta U_{i}(t)+ \frac{1}{2! Sci Rep 10, 15364 (2020). Duan et al.13 applied the Gaussian mixture model (GMM) to extract features from pulmonary nodules from CT images. The Marine Predators Algorithm (MPA)is a recently developed meta-heuristic algorithm that emulates the relation among the prey and predator in nature37. Alhamdulillah, glad to share that our paper entitled "Multi-class classification of brain tumor types from MR Images using EfficientNets" has been accepted for My education and internships have equipped me with strong technical skills in Python, deep learning models, machine learning classification, text classification, and more. Methods Med. Deep Learning Based Image Classification of Lungs Radiography for Detecting COVID-19 using a Deep CNN and ResNet 50 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. COVID-19 image classification using deep features and fractional-order marine predators algorithm. 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. The predator tries to catch the prey while the prey exploits the locations of its food. 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. Etymology. The Softmax activation function is used for this purpose because the output should be binary (positive COVID-19 negative COVID-19). 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 this paper, different Conv. Radiomics: extracting more information from medical images using advanced feature analysis. Fractional Differential Equations: An Introduction to Fractional Derivatives, Fdifferential Equations, to Methods of their Solution and Some of Their Applications Vol. 69, 4661 (2014). org (2015). In54, AlexNet pre-trained network was used to extract deep features then applied PCA to select the best features by eliminating highly correlated features. 1. Math. Eur. Hence, it was discovered that the VGG-16 based DTL model classified COVID-19 better than the VGG-19 based DTL model. Such methods might play a significant role as a computer-aided tool for image-based clinical diagnosis soon. arXiv preprint arXiv:2004.07054 (2020). One of the best methods of detecting. Feature selection using flower pollination optimization to diagnose lung cancer from ct images. where CF is the parameter that controls the step size of movement for the predator. MATH Besides, the binary classification between two classes of COVID-19 and normal chest X-ray is proposed. Therefore in MPA, for the first third of the total iterations, i.e., \(\frac{1}{3}t_{max}\)). The results show that, using only 6 epochs for training, the CNNs achieved very high performance on the classification task. Comput. Heidari, A. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition12511258 (2017). 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). 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. A CNN-transformer fusion network for COVID-19 CXR image classification A., Fan, H. & Abd ElAziz, M. Optimization method for forecasting confirmed cases of covid-19 in china. https://doi.org/10.1016/j.future.2020.03.055 (2020). The memory terms of the prey are updated at the end of each iteration based on first in first out concept. Multimedia Tools Appl. \end{aligned}$$, $$\begin{aligned} U_i(t+1)-U_i(t)=P.R\bigotimes S_i \end{aligned}$$, $$\begin{aligned} D ^{\delta } \left[ U_{i}(t+1)\right] =P.R\bigotimes S_i \end{aligned}$$, $$D^{\delta } \left[ {U_{i} (t + 1)} \right] = U_{i} (t + 1) + \sum\limits_{{k = 1}}^{m} {\frac{{( - 1)^{k} \Gamma (\delta + 1)U_{i} (t + 1 - k)}}{{\Gamma (k + 1)\Gamma (\delta - k + 1)}}} = P \cdot R \otimes S_{i} .$$, $$\begin{aligned} \begin{aligned} U(t+1)_{i}= - \sum _{k=1}^{m} \frac{(-1)^k\Gamma (\delta +1)U_{i}(t+1-k)}{\Gamma (k+1)\Gamma (\delta -k+1)} + P.R\bigotimes S_i. ), such as \(5\times 5\), \(3 \times 3\), \(1 \times 1\). More so, a combination of partial differential equations and deep learning was applied for medical image classification by10. The . Our results indicate that the VGG16 method outperforms . Imaging Syst. Chollet, F. Xception: Deep learning with depthwise separable convolutions. (22) can be written as follows: By taking into account the early mentioned relation in Eq. Building a custom CNN model: Identification of COVID-19 - Analytics Vidhya For this motivation, we utilize the FC concept with the MPA algorithm to boost the second step of the standard version of the algorithm. In this subsection, the performance of the proposed COVID-19 classification approach is compared to other CNN architectures. Frontiers | AI-Based Image Processing for COVID-19 Detection in Chest 6, right), our approach still provides an overall accuracy of 99.68%, putting it first with a slight advantage over MobileNet (99.67 %). Li, S., Chen, H., Wang, M., Heidari, A. faizancodes/COVID-19-X-Ray-Classification - GitHub 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 . Mutation: A mutation refers to a single change in a virus's genome (genetic code).Mutations happen frequently, but only sometimes change the characteristics of the virus. The evaluation outcomes demonstrate that ABC enhanced precision, and also it reduced the size of the features. Classification Covid-19 X-Ray Images | by Falah Gatea | Medium Very deep convolutional networks for large-scale image recognition. Also, in12, an Fs method based on SVM was proposed to detect Alzheimers disease from SPECT images. Both datasets shared some characteristics regarding the collecting sources. This stage can be mathematically implemented as below: In Eq. He, K., Zhang, X., Ren, S. & Sun, J. and pool layers, three fully connected layers, the last one performs classification. Decaf: A deep convolutional activation feature for generic visual recognition. where r is the run numbers. COVID-19-X-Ray-Classification Utilizing Deep Learning to detect COVID-19 and Viral Pneumonia from x-ray images Research Publication: https://dl.acm.org/doi/10.1145/3431804 Datasets used: COVID-19 Radiography Database COVID-19 10000 Images Related Research Papers: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7187882/ Types of coronavirus, their symptoms, and treatment - Medical News Today In ancient India, according to Aelian, it was . In Medical Imaging 2020: Computer-Aided Diagnosis, vol. Syst. Evaluation outcomes showed that GA based FS methods outperformed traditional approaches, such as filter based FS and traditional wrapper methods. The symbol \(R_B\) refers to Brownian motion. 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. However, the proposed IMF approach achieved the best results among the compared algorithms in least time. The proposed IMF approach is employed to select only relevant and eliminate unnecessary features. In Inception, there are different sizes scales convolutions (conv. In Dataset 2, FO-MPA also is reported as the highest classification accuracy with the best and mean measures followed by the BPSO. In Future of Information and Communication Conference, 604620 (Springer, 2020). If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Automatic CNN-based Chest X-Ray (CXR) image classification for detecting Covid-19 attracted so much attention. Slider with three articles shown per slide. Mirjalili, S. & Lewis, A. Decis. The whale optimization algorithm. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from . The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. Eurosurveillance 18, 20503 (2013). Research and application of fine-grained image classification based on SARS-CoV-2 Variant Classifications and Definitions PDF Classification of Covid-19 and Other Lung Diseases From Chest X-ray Images 35, 1831 (2017). Whereas, the worst algorithm was BPSO. Identifying Facemask-Wearing Condition Using Image Super-Resolution Ozturk, T. et al. 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 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. Adv. Since its structure consists of some parallel paths, all the paths use padding of 1 pixel to preserve the same height & width for the inputs and the outputs. The classification accuracy of MPA, WOA, SCA, and SGA are almost the same. Med. Keywords - Journal. \(r_1\) and \(r_2\) are the random index of the prey. 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. (iii) To implement machine learning classifiers for classification of COVID and non-COVID image classes. arXiv preprint arXiv:2003.13145 (2020). Extensive evaluation experiments had been carried out with a collection of two public X-ray images datasets. Some people say that the virus of COVID-19 is. Yousri, D. & Mirjalili, S. Fractional-order cuckoo search algorithm for parameter identification of the fractional-order chaotic, chaotic with noise and hyper-chaotic financial systems. The HGSO also was ranked last. 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). 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. https://doi.org/10.1155/2018/3052852 (2018). A systematic literature review of machine learning application in COVID A comprehensive study on classification of COVID-19 on - PubMed Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs. A joint segmentation and classification framework for COVID19 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. Li, H. etal. With the help of numerous algorithms in AI, modern COVID-19 cases can be detected and managed in a classified framework. Wu, Y.-H. etal. 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. It is important to detect positive cases early to prevent further spread of the outbreak. For fair comparison, each algorithms was performed (run) 25 times to produce statistically stable results.The results are listed in Tables3 and4. Nature 503, 535538 (2013). MathSciNet & Cao, J. Accordingly, the prey position is upgraded based the following equations. Coronavirus Disease (COVID-19): A primer for emergency physicians (2020) Summer Chavez et al. You have a passion for computer science and you are driven to make a difference in the research community? Objective: Lung image classification-assisted diagnosis has a large application market. The two datasets consist of X-ray COVID-19 images by international Cardiothoracic radiologist, researchers and others published on Kaggle. Google Scholar. Eng. The proposed approach was evaluated on two public COVID-19 X-ray datasets which achieves both high performance and reduction of computational complexity. Google Research, https://research.googleblog.com/2017/11/automl-for-large-scaleimage.html, Blog (2017). For the exploration stage, the weibull distribution has been applied rather than Brownian to bost the performance of the predator in stage 2 and the prey velocity in stage 1 based on the following formula: Where k, and \(\zeta\) are the scale and shape parameters. In this subsection, a comparison with relevant works is discussed. On the second dataset, dataset 2 (Fig. Propose similarity regularization for improving C. However, using medical imaging, chest CT, and chest X-ray scan can play a critical role in COVID-19 diagnosis. Computational image analysis techniques play a vital role in disease treatment and diagnosis. This paper reviews the recent progress of deep learning in COVID-19 images applications from five aspects; Firstly, 33 COVID-19 datasets and data enhancement methods are introduced; Secondly, COVID-19 classification methods . 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. Inceptions layer details and layer parameters of are given in Table1. The algorithm combines the assessment of image quality, digital image processing and deep learning for segmentation of the lung tissues and their classification. Biocybern. Ozturk et al. With accounting the first four previous events (\(m=4\)) from the memory data with derivative order \(\delta\), the position of prey can be modified as follow; Second: Adjusting \(R_B\) random parameter based on weibull distribution. 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 . 42, 6088 (2017). Inspired by our recent work38, where VGG-19 besides statistically enhanced Salp Swarm Algorithm was applied to select the best features for White Blood Cell Leukaemia classification. Kong, Y., Deng, Y. PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer. Johnson et al.31 applied the flower pollination algorithm (FPA) to select features from CT images of the lung, to detect lung cancers. 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. Also, As seen in Fig. The main purpose of Conv. An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm. (1): where \(O_k\) and \(E_k\) refer to the actual and the expected feature value, respectively. Comput. A properly trained CNN requires a lot of data and CPU/GPU time. Med. Average of the consuming time and the number of selected features in both datasets. 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. and M.A.A.A. }\delta (1-\delta )(2-\delta ) U_{i}(t-2)\\&\quad + \frac{1}{4! Article Shi, H., Li, H., Zhang, D., Cheng, C. & Cao, X. Metric learning Metric learning can create a space in which image features within the. First: prey motion based on FC the motion of the prey of Eq. Also, all other works do not give further statistics about their models 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. Appl. Cauchemez, S. et al. where \(R\in [0,1]\) is a random vector drawn from a uniform distribution and \(P=0.5\) is a constant number. [PDF] Detection and Severity Classification of COVID-19 in CT Images Continuing on my commitment to share small but interesting things in Google Cloud, this time I created a model for a Robertas Damasevicius. The optimum path forest (OPF) classifier was applied to classify pulmonary nodules based on CT images. . Article In general, MPA is a meta-heuristic technique that simulates the behavior of the prey and predator in nature37. Currently, a new coronavirus, called COVID-19, has spread to many countries, with over two million infected people or so-called confirmed cases. where \(ni_{j}\) is the importance of node j, while \(w_{j}\) refers to the weighted number of samples reaches the node j, also \(C_{j}\) determines the impurity value of node j. left(j) and right(j) are the child nodes from the left split and the right split on node j, respectively. Google Scholar. According to the formula10, the initial locations of the prey and predator can be defined as below: where the Elite matrix refers to the fittest predators. Also, in58 a new CNN architecture called EfficientNet was proposed, where more blocks were added on top of the model after applying normalization of images pixels intensity to the range (0 to 1). COVID-19 image classification using deep learning: Advances, challenges and opportunities COVID-19 image classification using deep learning: Advances, challenges and opportunities Comput Biol Med. We have used RMSprop optimizer for weight updates, cross entropy loss function and selected learning rate as 0.0001. AMERICAN JOURNAL OF EMERGENCY MEDICINE COVID-19: Facemask use prevalence in international airports in Asia, Europe and the Americas, March 2020 MPA simulates the main aim for most creatures that is searching for their foods, where a predator contiguously searches for food as well as the prey. 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. (18)(19) for the second half (predator) as represented below. Automated detection of alzheimers disease using brain mri imagesa study with various feature extraction techniques. Coronavirus disease (Covid-19) is an infectious disease that attacks the respiratory area caused by the severe acute . arXiv preprint arXiv:1704.04861 (2017). 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]. Therefore, in this paper, we propose a hybrid classification approach of COVID-19. Eq. The combination of Conv. (5). Efficient classification of white blood cell leukemia with improved swarm optimization of deep features. The variants of concern are Alpha, Beta, Gamma, and than the COVID-19 images. The test accuracy obtained for the model was 98%. 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. The Shearlet transform FS method showed better performances compared to several FS methods. A. arXiv preprint arXiv:2003.11597 (2020). Finally, the sum of the features importance value on each tree is calculated then divided by the total number of trees as in Eq. For example, Lambin et al.7 proposed an efficient approach called Radiomics to extract medical image features. implemented the deep neural networks and classification as well as prepared the related figures and manuscript text. Computer Vision - ECCV 2020 16th European Conference, Glasgow, UK New Images of Novel Coronavirus SARS-CoV-2 Now Available In Smart Intelligent Computing and Applications, 305313 (Springer, 2019). 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. 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. 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. This dataset consists of 219 COVID-19 positive images and 1341 negative COVID-19 images. kharrat and Mahmoud32proposed an FS method based on a hybrid of Simulated Annealing (SA) and GA to classify brain tumors using MRI. 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. In general, feature selection (FS) methods are widely employed in various applications of medical imaging applications. Biol. Expert Syst. 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. In our example the possible classifications are covid, normal and pneumonia. Also, image segmentation can extract critical features, including the shape of tissues, and texture5,6. ADS In addition, up to our knowledge, MPA has not applied to any real applications yet. 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. Al-qaness, M. A., Ewees, A. Image Underst. A NOVEL COMPARATIVE STUDY FOR AUTOMATIC THREE-CLASS AND FOUR-CLASS COVID-19 CLASSIFICATION ON X-RAY IMAGES USING DEEP LEARNING: Authors: Yaar, H. Ceylan, M. Keywords: Convolutional neural networks Covid-19 Deep learning Densenet201 Inceptionv3 Local binary pattern Local entropy X-ray chest classification Xception: Issue Date: 2022: Publisher: The lowest accuracy was obtained by HGSO in both measures. Havaei, M. et al. They were manually aggregated from various web based repositories into a machine learning (ML) friendly format with accompanying data loader code. By submitting a comment you agree to abide by our Terms and Community Guidelines. Adv. Comparison with other previous works using accuracy measure.