Vis. Chong, D. Y. et al. Among the FS methods, the metaheuristic techniques have been established their performance overall other FS methods when applied to classify medical images. Coronavirus Disease (COVID-19): A primer for emergency physicians (2020) Summer Chavez et al. The algorithm combines the assessment of image quality, digital image processing and deep learning for segmentation of the lung tissues and their classification. Dual feature selection and rebalancing strategy using metaheuristic optimization algorithms in x-ray image datasets. Netw. 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. \end{aligned} \end{aligned}$$, $$\begin{aligned} \begin{aligned} U_{i}(t+1)&= \frac{1}{1!} These images have been further used for the classification of COVID-19 and non-COVID-19 images using ResNet50 and AlexNet convolutional neural network (CNN) models. This study presents an investigation on 16 pretrained CNNs for classification of COVID-19 using a large public database of CT scans collected from COVID-19 patients and non-COVID-19 subjects. For fair comparison, each algorithms was performed (run) 25 times to produce statistically stable results.The results are listed in Tables3 and4. and pool layers, three fully connected layers, the last one performs classification. Very deep convolutional networks for large-scale image recognition. Our method is able to classify pneumonia from COVID-19 and visualize an abnormal area at the same time. Kharrat, A. They showed that analyzing image features resulted in more information that improved medical imaging. 41, 923 (2019). SMA is on the second place, While HGSO, SCA, and HHO came in the third to fifth place, respectively. et al. Med. The announcement confirmed that from May 8, following Japan's Golden Week holiday period, COVID-19 will be officially downgraded to Class 5, putting the virus on the same classification level as seasonal influenza. (2) To extract various textural features using the GLCM algorithm. (3), the importance of each feature is then calculated. We do not present a usable clinical tool for COVID-19 diagnosis, but offer a new, efficient approach to optimize deep learning-based architectures for medical image classification purposes. In some cases (as exists in this work), the dataset is limited, so it is not sufficient for building & training a CNN. The main purpose of Conv. Hashim, F. A., Houssein, E. H., Mabrouk, M. S., Al-Atabany, W. & Mirjalili, S. Henry gas solubility optimization: a novel physics-based algorithm. For instance,\(1\times 1\) conv. The evaluation confirmed that FPA based FS enhanced classification accuracy. Ozturk, T. et al. Image Underst. There are three main parameters for pooling, Filter size, Stride, and Max pool. Support Syst. Besides, all algorithms showed the same statistical stability in STD measure, except for HHO and HGSO. 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 . 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. 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. Some people say that the virus of COVID-19 is. (18)(19) for the second half (predator) as represented below. Sahlol, A. T., Kollmannsberger, P. & Ewees, A. & Mirjalili, S. Slime mould algorithm: A new method for stochastic optimization. Multimedia Tools Appl. Article Improving the ranking quality of medical image retrieval using a genetic feature selection method. 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). Mobilenets: Efficient convolutional neural networks for mobile vision applications. This algorithm is tested over a global optimization problem. Deep Learning Based Image Classification of Lungs Radiography for Detecting COVID-19 using a Deep CNN and ResNet 50 Huang, P. et al. Med. The MPA starts with the initialization phase and then passing by other three phases with respect to the rational velocity among the prey and the predator. what medical images are commonly used for COVID-19 classification and what are the methods for COVID-19 classification. Tree based classifier are the most popular method to calculate feature importance to improve the classification since they have high accuracy, robustness, and simple38. With the help of numerous algorithms in AI, modern COVID-19 cases can be detected and managed in a classified framework. Regarding the consuming time as in Fig. In general, feature selection (FS) methods are widely employed in various applications of medical imaging applications. Wu, Y.-H. etal. 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. The symbol \(R_B\) refers to Brownian motion. 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. Finally, the sum of the features importance value on each tree is calculated then divided by the total number of trees as in Eq. Table3 shows the numerical results of the feature selection phase for both datasets. Detection of lung cancer on chest ct images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. and A.A.E. Furthermore, deep learning using CNN is considered one of the best choices in medical imaging applications20, especially classification. Methods: We employed a public dataset acquired from 20 COVID-19 patients, which . 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. 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. 11314, 113142S (International Society for Optics and Photonics, 2020). Image Anal. Moreover, other COVID-19 positive images were added by the Italian Society of Medical and Interventional Radiology (SIRM) COVID-19 Database45. ADS 40, 2339 (2020). Access through your institution. (14)-(15) are implemented in the first half of the agents that represent the exploitation. The proposed segmentation method is capable of dealing with the problem of diffuse lung borders in CXR images of patients with COVID-19 severe or critical. They used K-Nearest Neighbor (kNN) to classify x-ray images collected from Montgomery dataset, and it showed good performances. The whale optimization algorithm. IEEE Trans. The experimental results and comparisons with other works are presented inResults and discussion section, while they are discussed in Discussion section Finally, the conclusion is described in Conclusion section. It can be concluded that FS methods have proven their advantages in different medical imaging applications19. . They compared the BA to PSO, and the comparison outcomes showed that BA had better performance. In this paper, different Conv. 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). Harris hawks optimization: algorithm and applications. Deep cnns for microscopic image classification by exploiting transfer learning and feature concatenation. 0.9875 and 0.9961 under binary and multi class classifications respectively. }, \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. Also, other recent published works39, who combined a CNN architecture with Weighted Symmetric Uncertainty (WSU) to select optimal features for traffic classification. 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. arXiv preprint arXiv:2004.05717 (2020). Lambin, P. et al. EMRes-50 model . 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. Toaar, M., Ergen, B. Heidari, A. 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). A., Fan, H. & Abd ElAziz, M. Optimization method for forecasting confirmed cases of covid-19 in china. 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. Four measures for the proposed method and the compared algorithms are listed. Two real datasets about COVID-19 patients are studied in this paper. The proposed approach selected successfully 130 and 86 out of 51 K features extracted by inception from dataset 1 and dataset 2, while improving classification accuracy at the same time. 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. Sci Rep 10, 15364 (2020). 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. In 2018 IEEE International Symposium on Circuits and Systems (ISCAS), 15 (IEEE, 2018). Stage 2 has been executed in the second third of the total number of iterations when \(\frac{1}{3}t_{max}< t< \frac{2}{3}t_{max}\). Comput. 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. They shared some parameters, such as the total number of iterations and the number of agents which were set to 20 and 15, respectively. Article 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. 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).. Brain tumor segmentation with deep neural networks. HIGHLIGHTS who: Yuan Jian and Qin Xiao from the Fukuoka University, Japan have published the Article: Research and Application of Fine-Grained Image Classification Based on Small Collar Dataset, in the Journal: (JOURNAL) what: MC-Loss drills down on the channels to effectively navigate the model, focusing on different distinguishing regions and highlighting diverse features. 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). (2018). The updating operation repeated until reaching the stop condition. 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. Continuing on my commitment to share small but interesting things in Google Cloud, this time I created a model for a Li et al.34 proposed a self-adaptive bat algorithm (BA) to address two problems in lung X-ray images, rebalancing, and feature selection. 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 . In COVID19 triage, DB-YNet is a promising tool to assist physicians in the early identification of COVID19 infected patients for quick clinical interventions. 43, 302 (2019). & Carlsson, S. Cnn features off-the-shelf: an astounding baseline for recognition. Software available from tensorflow. Stage 2: The prey/predator in this stage begin exploiting the best location that detects for their foods. The given Kaggle dataset consists of chest CT scan images of patients suffering from the novel COVID-19, other pulmonary disorders, and those of healthy patients. \delta U_{i}(t)+ \frac{1}{2! 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. \(r_1\) and \(r_2\) are the random index of the prey. Kong, Y., Deng, Y. Chong et al.8 proposed an FS model, called Robustness-Driven FS (RDFS) to select futures from lung CT images to classify the patterns of fibrotic interstitial lung diseases. 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. 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). where r is the run numbers. On January 20, 2023, Japanese Prime Minister Fumio Kishida announced that the country would be downgrading the COVID-19 classification. 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. Knowl. is applied before larger sized kernels are applied to reduce the dimension of the channels, which accordingly, reduces the computation cost. Correspondence to Early diagnosis, timely treatment, and proper confinement of the infected patients are some possible ways to control the spreading of . Eurosurveillance 18, 20503 (2013). 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. The optimum path forest (OPF) classifier was applied to classify pulmonary nodules based on CT images. (22) can be written as follows: By taking into account the early mentioned relation in Eq. Our results indicate that the VGG16 method outperforms . Alhamdulillah, glad to share that our paper entitled "Multi-class classification of brain tumor types from MR Images using EfficientNets" has been accepted for 2022 May;144:105350. doi: 10.1016/j.compbiomed.2022.105350. Appl. This stage can be mathematically implemented as below: In Eq. Google Scholar. We are hiring! & Dai, Q. Discriminative clustering and feature selection for brain mri segmentation. Also, they require a lot of computational resources (memory & storage) for building & training. They also used the SVM to classify lung CT images. PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer. The results show that, using only 6 epochs for training, the CNNs achieved very high performance on the classification task. They applied the SVM classifier with and without RDFS. 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. Jcs: An explainable covid-19 diagnosis system by joint classification and segmentation. Google Scholar. 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. Future Gener. Eur. These datasets contain hundreds of frontal view X-rays and considered the largest public resource for COVID-19 image data. Sci. Hence, the FC memory is applied during updating the prey locating in the second step of the algorithm to enhance the exploitation stage. 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 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. 111, 300323. 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. All authors discussed the results and wrote the manuscript together.
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