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Antar S, Abd El-Sattar HKH, Abdel-Rahman MH, F M Ghaleb F. COVID-19 infection segmentation using hybrid deep learning and image processing techniques. Sci Rep 2023; 13:22737. [PMID: 38123587 PMCID: PMC10733411 DOI: 10.1038/s41598-023-49337-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 12/07/2023] [Indexed: 12/23/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) epidemic has become a worldwide problem that continues to affect people's lives daily, and the early diagnosis of COVID-19 has a critical importance on the treatment of infected patients for medical and healthcare organizations. To detect COVID-19 infections, medical imaging techniques, including computed tomography (CT) scan images and X-ray images, are considered some of the helpful medical tests that healthcare providers carry out. However, in addition to the difficulty of segmenting contaminated areas from CT scan images, these approaches also offer limited accuracy for identifying the virus. Accordingly, this paper addresses the effectiveness of using deep learning (DL) and image processing techniques, which serve to expand the dataset without the need for any augmentation strategies, and it also presents a novel approach for detecting COVID-19 virus infections in lung images, particularly the infection prediction issue. In our proposed method, to reveal the infection, the input images are first preprocessed using a threshold then resized to 128 × 128. After that, a density heat map tool is used for coloring the resized lung images. The three channels (red, green, and blue) are then separated from the colored image and are further preprocessed through image inverse and histogram equalization, and are subsequently fed, in independent directions, into three separate U-Nets with the same architecture for segmentation. Finally, the segmentation results are combined and run through a convolution layer one by one to get the detection. Several evaluation metrics using the CT scan dataset were used to measure the performance of the proposed approach in comparison with other state-of-the-art techniques in terms of accuracy, sensitivity, precision, and the dice coefficient. The experimental results of the proposed approach reached 99.71%, 0.83, 0.87, and 0.85, respectively. These results show that coloring the CT scan images dataset and then dividing each image into its RGB image channels can enhance the COVID-19 detection, and it also increases the U-Net power in the segmentation when merging the channel segmentation results. In comparison to other existing segmentation techniques employing bigger 512 × 512 images, this study is one of the few that can rapidly and correctly detect the COVID-19 virus with high accuracy on smaller 128 × 128 images using the metrics of accuracy, sensitivity, precision, and dice coefficient.
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Affiliation(s)
- Samar Antar
- Computer Science Division, Department of Mathematics, Faculty of Science, Ain Shams University, Abbassia, Cairo, 11566, Egypt
| | | | - Mohammad H Abdel-Rahman
- Computer Science Division, Department of Mathematics, Faculty of Science, Ain Shams University, Abbassia, Cairo, 11566, Egypt
| | - Fayed F M Ghaleb
- Computer Science Division, Department of Mathematics, Faculty of Science, Ain Shams University, Abbassia, Cairo, 11566, Egypt
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Healthcare Engineering JO. Retracted: Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models. JOURNAL OF HEALTHCARE ENGINEERING 2023; 2023:9812058. [PMID: 37860421 PMCID: PMC10584605 DOI: 10.1155/2023/9812058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 10/10/2023] [Indexed: 10/21/2023]
Abstract
[This retracts the article DOI: 10.1155/2022/5329014.].
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Wang L, Zhou H, Xu N, Liu Y, Jiang X, Li S, Feng C, Xu H, Deng K, Song J. A general approach for automatic segmentation of pneumonia, pulmonary nodule, and tuberculosis in CT images. iScience 2023; 26:107005. [PMID: 37534183 PMCID: PMC10391673 DOI: 10.1016/j.isci.2023.107005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 04/27/2023] [Accepted: 05/26/2023] [Indexed: 08/04/2023] Open
Abstract
Proposing a general segmentation approach for lung lesions, including pulmonary nodules, pneumonia, and tuberculosis, in CT images will improve efficiency in radiology. However, the performance of generative adversarial networks is hampered by the limited availability of annotated samples and the catastrophic forgetting of the discriminator, whereas the universality of traditional morphology-based methods is insufficient for segmenting diverse lung lesions. A cascaded dual-attention network with a context-aware pyramid feature extraction module was designed to address these challenges. A self-supervised rotation loss was designed to mitigate discriminator forgetting. The proposed model achieved Dice coefficients of 70.92, 73.55, and 68.52% on multi-center pneumonia, lung nodule, and tuberculosis test datasets, respectively. No significant decrease in accuracy was observed (p > 0.10) when a small training sample size was used. The cyclic training of the discriminator was reduced with self-supervised rotation loss (p < 0.01). The proposed approach is promising for segmenting multiple lung lesion types in CT images.
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Affiliation(s)
- Lu Wang
- Department of Library, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China
- School of Health Management, China Medical University, Shenyang, Liaoning 110122, China
| | - He Zhou
- School of Health Management, China Medical University, Shenyang, Liaoning 110122, China
| | - Nan Xu
- School of Health Management, China Medical University, Shenyang, Liaoning 110122, China
| | - Yuchan Liu
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, USTC Hefei, Anhui 230036, China
| | - Xiran Jiang
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning 110122, China
| | - Shu Li
- School of Health Management, China Medical University, Shenyang, Liaoning 110122, China
| | - Chaolu Feng
- Key Laboratory of Intelligent Computing in Medical Image (MIIC), Ministry of Education, Shenyang, Liaoning 110169, China
| | - Hainan Xu
- Department of Obstetrics and Gynecology, Pelvic Floor Disease Diagnosis and Treatment Center, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China
| | - Kexue Deng
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, USTC Hefei, Anhui 230036, China
| | - Jiangdian Song
- School of Health Management, China Medical University, Shenyang, Liaoning 110122, China
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Application of a novel deep learning technique using CT images for COVID-19 diagnosis on embedded systems. ALEXANDRIA ENGINEERING JOURNAL 2023; 74:345-358. [PMCID: PMC10183629 DOI: 10.1016/j.aej.2023.05.036] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 04/24/2023] [Accepted: 05/08/2023] [Indexed: 11/04/2023]
Abstract
Problem A novel coronavirus (COVID-19) has created a worldwide pneumonia epidemic, and it's important to make a computer-aided way for doctors to use computed tomography (CT) images to find people with COVID-19 as soon as possible. Aim: A fully automated, novel deep-learning method for diagnosis and prognostic analysis of COVID-19 on the embedded system is presented. Methods In this study, CT scans are utilized to identify individuals with COVID-19, pneumonia, or normal class. To achieve classification two pre-trained CNN models, namely ResNet50 and MobileNetv2, which are commonly used for image classification tasks. Additionally, a novel CNN architecture called CovidxNet-CT is introduced specifically designed for COVID-19 diagnosis using three classes of CT scans. To evaluate the effectiveness of the proposed method, k-fold cross-validation is employed, which is a common approach to estimate the performance of deep learning. The study is also evaluated the proposed method on two embedded system platforms, Jetson Nano and Tx2, to demonstrate its feasibility for deployment in resource-constrained environments. Results With an average accuracy of %98.83 and an AUC of 0.988, the system is trained and verified using a 4 fold cross-validation approach. Conclusion The optimistic outcomes from the investigation propose that CovidxNet-CT has the capacity to support radiologists and contribute towards the efforts to combat COVID-19. This study proposes a fully automated, deep-learning-based method for COVID-19 diagnosis and prognostic analysis that is specifically designed for use on embedded systems.
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Wu Y, Qi Q, Qi S, Yang L, Wang H, Yu H, Li J, Wang G, Zhang P, Liang Z, Chen R. Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans. Comput Biol Med 2023; 154:106567. [PMID: 36738705 PMCID: PMC9869624 DOI: 10.1016/j.compbiomed.2023.106567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 12/30/2022] [Accepted: 01/22/2023] [Indexed: 01/24/2023]
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) and community-acquired pneumonia (CAP) present a high degree of similarity in chest computed tomography (CT) images. Therefore, a procedure for accurately and automatically distinguishing between them is crucial. METHODS A deep learning method for distinguishing COVID-19 from CAP is developed using maximum intensity projection (MIP) images from CT scans. LinkNet is employed for lung segmentation of chest CT images. MIP images are produced by superposing the maximum gray of intrapulmonary CT values. The MIP images are input into a capsule network for patient-level pred iction and diagnosis of COVID-19. The network is trained using 333 CT scans (168 COVID-19/165 CAP) and validated on three external datasets containing 3581 CT scans (2110 COVID-19/1471 CAP). RESULTS LinkNet achieves the highest Dice coefficient of 0.983 for lung segmentation. For the classification of COVID-19 and CAP, the capsule network with the DenseNet-121 feature extractor outperforms ResNet-50 and Inception-V3, achieving an accuracy of 0.970 on the training dataset. Without MIP or the capsule network, the accuracy decreases to 0.857 and 0.818, respectively. Accuracy scores of 0.961, 0.997, and 0.949 are achieved on the external validation datasets. The proposed method has higher or comparable sensitivity compared with ten state-of-the-art methods. CONCLUSIONS The proposed method illustrates the feasibility of applying MIP images from CT scans to distinguish COVID-19 from CAP using capsule networks. MIP images provide conspicuous benefits when exploiting deep learning to detect COVID-19 lesions from CT scans and the capsule network improves COVID-19 diagnosis.
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Affiliation(s)
- Yanan Wu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Qianqian Qi
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China.
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Liming Yang
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China.
| | - Hanlin Wang
- Department of Radiology, General Hospital of the Yangtze River Shipping, Wuhan, China.
| | - Hui Yu
- General Practice Center, The Seventh Affiliated Hospital, Southern Medical University, Guangzhou, China.
| | - Jianpeng Li
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, China.
| | - Gang Wang
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, China.
| | - Ping Zhang
- Department of Pulmonary and Critical Care Medicine, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, China.
| | - Zhenyu Liang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
| | - Rongchang Chen
- Key Laboratory of Respiratory Disease of Shenzhen, Shenzhen Institute of Respiratory Disease, Shenzhen People's Hospital (Second Affiliated Hospital of Jinan University, First Affiliated Hospital of South University of Science and Technology of China), Shenzhen, China.
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Rao Y, Lv Q, Zeng S, Yi Y, Huang C, Gao Y, Cheng Z, Sun J. COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold. Biomed Signal Process Control 2023; 81:104486. [PMID: 36505089 PMCID: PMC9721288 DOI: 10.1016/j.bspc.2022.104486] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 11/23/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022]
Abstract
The ground glass opacity (GGO) of the lung is one of the essential features of COVID-19. The GGO in computed tomography (CT) images has various features and low-intensity contrast between the GGO and edge structures. These problems pose significant challenges for segmenting the GGO. To tackle these problems, we propose a new threshold method for accurate segmentation of GGO. Specifically, we offer a framework for adjusting the threshold parameters according to the image contrast. Three functions include Attention mechanism threshold, Contour equalization, and Lung segmentation (ACL). The lung is divided into three areas using the attention mechanism threshold. Further, the segmentation parameters of the attention mechanism thresholds of the three parts are adaptively adjusted according to the image contrast. Only the segmentation regions restricted by the lung segmentation results are retained. Extensive experiments on four COVID datasets show that ACL can segment GGO images at low contrast well. Compared with the state-of-the-art methods, the similarity Dice of the ACL segmentation results is improved by 8.9%, the average symmetry surface distance ASD is reduced by 23%, and the required computational power F L O P s are only 0.09% of those of deep learning models. For GGO segmentation, ACL is more lightweight, and the accuracy is higher. Code will be released at https://github.com/Lqs-github/ACL.
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Affiliation(s)
- Yunbo Rao
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Qingsong Lv
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Shaoning Zeng
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, 313000, China
| | - Yuling Yi
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Cheng Huang
- Fifth Clinical College of Chongqing Medical University, Chongqing, 402177, China
| | - Yun Gao
- Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
| | - Zhanglin Cheng
- Advanced Technology Chinese Academy of Sciences, Shenzhen, 610042, China
| | - Jihong Sun
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310014, China
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Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:4776770. [PMID: 36864930 PMCID: PMC9974276 DOI: 10.1155/2023/4776770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 07/31/2022] [Accepted: 08/16/2022] [Indexed: 02/25/2023]
Abstract
Malfunctions in the immune system cause multiple sclerosis (MS), which initiates mild to severe nerve damage. MS will disturb the signal communication between the brain and other body parts, and early diagnosis will help reduce the harshness of MS in humankind. Magnetic resonance imaging (MRI) supported MS detection is a standard clinical procedure in which the bio-image recorded with a chosen modality is considered to assess the severity of the disease. The proposed research aims to implement a convolutional neural network (CNN) supported scheme to detect MS lesions in the chosen brain MRI slices. The stages of this framework include (i) image collection and resizing, (ii) deep feature mining, (iii) hand-crafted feature mining, (iii) feature optimization with firefly algorithm, and (iv) serial feature integration and classification. In this work, five-fold cross-validation is executed, and the final result is considered for the assessment. The brain MRI slices with/without the skull section are examined separately, presenting the attained results. The experimental outcome of this study confirms that the VGG16 with random forest (RF) classifier offered a classification accuracy of >98% MRI with skull, and VGG16 with K-nearest neighbor (KNN) provided an accuracy of >98% without the skull.
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8
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Mohammed MA, Abdulkareem KH, Dinar AM, Zapirain BG. Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review. Diagnostics (Basel) 2023; 13:diagnostics13040664. [PMID: 36832152 PMCID: PMC9955380 DOI: 10.3390/diagnostics13040664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 02/05/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023] Open
Abstract
This research aims to review and evaluate the most relevant scientific studies about deep learning (DL) models in the omics field. It also aims to realize the potential of DL techniques in omics data analysis fully by demonstrating this potential and identifying the key challenges that must be addressed. Numerous elements are essential for comprehending numerous studies by surveying the existing literature. For example, the clinical applications and datasets from the literature are essential elements. The published literature highlights the difficulties encountered by other researchers. In addition to looking for other studies, such as guidelines, comparative studies, and review papers, a systematic approach is used to search all relevant publications on omics and DL using different keyword variants. From 2018 to 2022, the search procedure was conducted on four Internet search engines: IEEE Xplore, Web of Science, ScienceDirect, and PubMed. These indexes were chosen because they offer enough coverage and linkages to numerous papers in the biological field. A total of 65 articles were added to the final list. The inclusion and exclusion criteria were specified. Of the 65 publications, 42 are clinical applications of DL in omics data. Furthermore, 16 out of 65 articles comprised the review publications based on single- and multi-omics data from the proposed taxonomy. Finally, only a small number of articles (7/65) were included in papers focusing on comparative analysis and guidelines. The use of DL in studying omics data presented several obstacles related to DL itself, preprocessing procedures, datasets, model validation, and testbed applications. Numerous relevant investigations were performed to address these issues. Unlike other review papers, our study distinctly reflects different observations on omics with DL model areas. We believe that the result of this study can be a useful guideline for practitioners who look for a comprehensive view of the role of DL in omics data analysis.
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Affiliation(s)
- Mazin Abed Mohammed
- College of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq
- eVIDA Lab, University of Deusto, 48007 Bilbao, Spain
- Correspondence: (M.A.M.); (B.G.Z.)
| | - Karrar Hameed Abdulkareem
- College of Agriculture, Al-Muthanna University, Samawah 66001, Iraq
- College of Engineering, University of Warith Al-Anbiyaa, Karbala 56001, Iraq
| | - Ahmed M. Dinar
- Computer Engineering Department, University of Technology- Iraq, Baghdad 19006, Iraq
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Li W, Deng X, Zhao H, Shao H, Jiang Y. COVID-19 diagnosis prediction using classical-to-quantum ensemble model with transfer learning for CT scan images. THE IMAGING SCIENCE JOURNAL 2023. [DOI: 10.1080/13682199.2022.2160887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Wenqian Li
- School of Computer, Jiangsu University of Science and Technology, Zhenjiang, People’s Republic of China
| | - Xing Deng
- School of Computer, Jiangsu University of Science and Technology, Zhenjiang, People’s Republic of China
| | - Haorong Zhao
- School of Computer, Jiangsu University of Science and Technology, Zhenjiang, People’s Republic of China
| | - Haijian Shao
- School of Computer, Jiangsu University of Science and Technology, Zhenjiang, People’s Republic of China
- Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, NV, USA
| | - Yingtao Jiang
- Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, NV, USA
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10
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Deep Learning Convolutional Neural Network for SARS-CoV-2 Detection Using Chest X-Ray Images. ACTA INFORMATICA PRAGENSIA 2023. [DOI: 10.18267/j.aip.205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/20/2023] Open
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EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images. Diagnostics (Basel) 2022; 12:diagnostics12112569. [DOI: 10.3390/diagnostics12112569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/13/2022] [Accepted: 10/18/2022] [Indexed: 11/16/2022] Open
Abstract
The COVID-19 pandemic has had a significant impact on many lives and the economies of many countries since late December 2019. Early detection with high accuracy is essential to help break the chain of transmission. Several radiological methodologies, such as CT scan and chest X-ray, have been employed in diagnosing and monitoring COVID-19 disease. Still, these methodologies are time-consuming and require trial and error. Machine learning techniques are currently being applied by several studies to deal with COVID-19. This study exploits the latent embeddings of variational autoencoders combined with ensemble techniques to propose three effective EVAE-Net models to detect COVID-19 disease. Two encoders are trained on chest X-ray images to generate two feature maps. The feature maps are concatenated and passed to either a combined or individual reparameterization phase to generate latent embeddings by sampling from a distribution. The latent embeddings are concatenated and passed to a classification head for classification. The COVID-19 Radiography Dataset from Kaggle is the source of chest X-ray images. The performances of the three models are evaluated. The proposed model shows satisfactory performance, with the best model achieving 99.19% and 98.66% accuracy on four classes and three classes, respectively.
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12
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Guhan B, Almutairi L, Sowmiya S, Snekhalatha U, Rajalakshmi T, Aslam SM. Automated system for classification of COVID-19 infection from lung CT images based on machine learning and deep learning techniques. Sci Rep 2022; 12:17417. [PMID: 36257964 PMCID: PMC9579174 DOI: 10.1038/s41598-022-20804-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 09/19/2022] [Indexed: 01/12/2023] Open
Abstract
The objectives of our proposed study were as follows: First objective is to segment the CT images using a k-means clustering algorithm for extracting the region of interest and to extract textural features using gray level co-occurrence matrix (GLCM). Second objective is to implement machine learning classifiers such as Naïve bayes, bagging and Reptree to classify the images into two image classes namely COVID and non-COVID and to compare the performance of the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet with that of the proposed machine learning classifiers. Our dataset consists of 100 COVID and non-COVID images which are pre-processed and segmented with our proposed algorithm. Following the feature extraction process, three machine learning classifiers (Naive Bayes, Bagging, and REPTree) were used to classify the normal and covid patients. We had implemented the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet for comparing their performance with machine learning classifiers. In machine learning, the Naive Bayes classifier achieved the highest accuracy of 97%, whereas the ResNet50 CNN model attained the highest accuracy of 99%. Hence the deep learning networks outperformed well compared to the machine learning techniques in the classification of Covid-19 images.
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Affiliation(s)
- Bhargavee Guhan
- grid.412742.60000 0004 0635 5080Department of Biomedical Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu 603203 India
| | - Laila Almutairi
- grid.449051.d0000 0004 0441 5633Department of Computer Engineering, College of Computer and Information Sciences, Majmaah University, Al Majmaah, 11952 Saudi Arabia
| | - S. Sowmiya
- grid.412742.60000 0004 0635 5080Department of Biomedical Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu 603203 India
| | - U. Snekhalatha
- grid.412742.60000 0004 0635 5080Department of Biomedical Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu 603203 India
| | - T. Rajalakshmi
- grid.412742.60000 0004 0635 5080Department of Electronics and Communication Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India
| | - Shabnam Mohamed Aslam
- grid.449051.d0000 0004 0441 5633Department of Information Technology, College of Computer and Information Sciences, Majmaah University, Al Majmaah, 11952 Saudi Arabia
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Manocha A, Bhatia M. A novel deep fusion strategy for COVID-19 prediction using multimodality approach. COMPUTERS & ELECTRICAL ENGINEERING : AN INTERNATIONAL JOURNAL 2022; 103:108274. [PMID: 35938050 PMCID: PMC9346103 DOI: 10.1016/j.compeleceng.2022.108274] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 07/22/2022] [Accepted: 07/25/2022] [Indexed: 05/26/2023]
Abstract
Over the last two years, the novel coronavirus has become a significant threat to the health of the public, and numerous approaches are developed to determine the symptoms of COVID-19. To deal with the complex symptoms of COVID-19, a Deep Learning-assisted Multi-modal Data Analysis (DMDA) approach is introduced to determine COVID-19 symptoms by utilizing acoustic and image-based data. Furthermore, the classified events are forwarded to the proposed Dynamic Fusion Strategy (DFS) for confirming the health status of the individual. Initially, the performance of the proposed solution is evaluated on both acoustic and image-based samples and the proposed solution attains the maximum accuracy of 96.88% and 98.76%, respectively. Similarly, the DFS has achieved an overall symptom determination accuracy of 98.72% which is highly acceptable for decision-making. Moreover, the proposed solution shows high reliability with an accuracy of 95.64% even in absence of any one of the data modalities during testing.
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Affiliation(s)
- Ankush Manocha
- Lovely Professional University, Phagwara, 144411, Punjab, India
| | - Munish Bhatia
- Lovely Professional University, Phagwara, 144411, Punjab, India
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Mukhlif AA, Al-Khateeb B, Mohammed MA. An extensive review of state-of-the-art transfer learning techniques used in medical imaging: Open issues and challenges. JOURNAL OF INTELLIGENT SYSTEMS 2022. [DOI: 10.1515/jisys-2022-0198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
Deep learning techniques, which use a massive technology known as convolutional neural networks, have shown excellent results in a variety of areas, including image processing and interpretation. However, as the depth of these networks grows, so does the demand for a large amount of labeled data required to train these networks. In particular, the medical field suffers from a lack of images because the procedure for obtaining labeled medical images in the healthcare field is difficult, expensive, and requires specialized expertise to add labels to images. Moreover, the process may be prone to errors and time-consuming. Current research has revealed transfer learning as a viable solution to this problem. Transfer learning allows us to transfer knowledge gained from a previous process to improve and tackle a new problem. This study aims to conduct a comprehensive survey of recent studies that dealt with solving this problem and the most important metrics used to evaluate these methods. In addition, this study identifies problems in transfer learning techniques and highlights the problems of the medical dataset and potential problems that can be addressed in future research. According to our review, many researchers use pre-trained models on the Imagenet dataset (VGG16, ResNet, Inception v3) in many applications such as skin cancer, breast cancer, and diabetic retinopathy classification tasks. These techniques require further investigation of these models, due to training them on natural, non-medical images. In addition, many researchers use data augmentation techniques to expand their dataset and avoid overfitting. However, not enough studies have shown the effect of performance with or without data augmentation. Accuracy, recall, precision, F1 score, receiver operator characteristic curve, and area under the curve (AUC) were the most widely used measures in these studies. Furthermore, we identified problems in the datasets for melanoma and breast cancer and suggested corresponding solutions.
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Affiliation(s)
- Abdulrahman Abbas Mukhlif
- Computer Science Department, College of Computer Science and Information Technology, University of Anbar , 31001 , Ramadi , Anbar , Iraq
| | - Belal Al-Khateeb
- Computer Science Department, College of Computer Science and Information Technology, University of Anbar , 31001 , Ramadi , Anbar , Iraq
| | - Mazin Abed Mohammed
- Computer Science Department, College of Computer Science and Information Technology, University of Anbar , 31001 , Ramadi , Anbar , Iraq
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15
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Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID-19 Diagnostic Model. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1307944. [PMID: 35996653 PMCID: PMC9392599 DOI: 10.1155/2022/1307944] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Revised: 03/16/2022] [Accepted: 07/19/2022] [Indexed: 02/07/2023]
Abstract
Due to the COVID-19 pandemic, computerized COVID-19 diagnosis studies are proliferating. The diversity of COVID-19 models raises the questions of which COVID-19 diagnostic model should be selected and which decision-makers of healthcare organizations should consider performance criteria. Because of this, a selection scheme is necessary to address all the above issues. This study proposes an integrated method for selecting the optimal deep learning model based on a novel crow swarm optimization algorithm for COVID-19 diagnosis. The crow swarm optimization is employed to find an optimal set of coefficients using a designed fitness function for evaluating the performance of the deep learning models. The crow swarm optimization is modified to obtain a good selected coefficient distribution by considering the best average fitness. We have utilized two datasets: the first dataset includes 746 computed tomography images, 349 of them are of confirmed COVID-19 cases and the other 397 are of healthy individuals, and the second dataset are composed of unimproved computed tomography images of the lung for 632 positive cases of COVID-19 with 15 trained and pretrained deep learning models with nine evaluation metrics are used to evaluate the developed methodology. Among the pretrained CNN and deep models using the first dataset, ResNet50 has an accuracy of 91.46% and a F1-score of 90.49%. For the first dataset, the ResNet50 algorithm is the optimal deep learning model selected as the ideal identification approach for COVID-19 with the closeness overall fitness value of 5715.988 for COVID-19 computed tomography lung images case considered differential advancement. In contrast, the VGG16 algorithm is the optimal deep learning model is selected as the ideal identification approach for COVID-19 with the closeness overall fitness value of 5758.791 for the second dataset. Overall, InceptionV3 had the lowest performance for both datasets. The proposed evaluation methodology is a helpful tool to assist healthcare managers in selecting and evaluating the optimal COVID-19 diagnosis models based on deep learning.
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16
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Fang L, Wang X. COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images. Biocybern Biomed Eng 2022; 42:977-994. [PMID: 35945982 PMCID: PMC9353669 DOI: 10.1016/j.bbe.2022.07.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 07/10/2022] [Accepted: 07/31/2022] [Indexed: 12/23/2022]
Abstract
Corona virus disease 2019 (COVID-19) testing relies on traditional screening methods, which require a lot of manpower and material resources. Recently, to effectively reduce the damage caused by radiation and enhance effectiveness, deep learning of classifying COVID-19 negative and positive using the mixed dataset by CT and X-rays images have achieved remarkable research results. However, the details presented on CT and X-ray images have pathological diversity and similarity features, thus increasing the difficulty for physicians to judge specific cases. On this basis, this paper proposes a novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images. To solve the problem of feature similarity between lung diseases and COVID-19, the extracted features are enhanced by an adaptive region enhancement algorithm. Besides, the depth network based on the residual blocks and the dense blocks is trained and tested. On the one hand, the residual blocks effectively improve the accuracy of the model and the non-linear COVID-19 features are obtained by cross-layer link. On the other hand, the dense blocks effectively improve the robustness of the model by connecting local and abstract information. On mixed X-ray and CT datasets, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under curve (AUC), and accuracy can all reach 0.99. On the basis of respecting patient privacy and ethics, the proposed algorithm using the mixed dataset from real cases can effectively assist doctors in performing the accurate COVID-19 negative and positive classification to determine the infection status of patients.
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Affiliation(s)
- Lingling Fang
- Department of Computing and Information Technology, Liaoning Normal University, Dalian City, Liaoning Province, China
| | - Xin Wang
- Department of Computing and Information Technology, Liaoning Normal University, Dalian City, Liaoning Province, China
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17
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Performance Analysis for COVID-19 Diagnosis Using Custom and State-of-the-Art Deep Learning Models. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12136364] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The modern scientific world continuously endeavors to battle and devise solutions for newly arising pandemics. One such pandemic which has turned the world’s accustomed routine upside down is COVID-19: it has devastated the world economy and destroyed around 45 million lives, globally. Governments and scientists have been on the front line, striving towards the diagnosis and engineering of a vaccination for the said virus. COVID-19 can be diagnosed using artificial intelligence more accurately than traditional methods using chest X-rays. This research involves an evaluation of the performance of deep learning models for COVID-19 diagnosis using chest X-ray images from a dataset containing the largest number of COVID-19 images ever used in the literature, according to the best of the authors’ knowledge. The size of the utilized dataset is about 4.25 times the maximum COVID-19 chest X-ray image dataset used in the explored literature. Further, a CNN model was developed, named the Custom-Model in this study, for evaluation against, and comparison to, the state-of-the-art deep learning models. The intention was not to develop a new high-performing deep learning model, but rather to evaluate the performance of deep learning models on a larger COVID-19 chest X-ray image dataset. Moreover, Xception- and MobilNetV2- based models were also used for evaluation purposes. The criteria for evaluation were based on accuracy, precision, recall, F1 score, ROC curves, AUC, confusion matrix, and macro and weighted averages. Among the deployed models, Xception was the top performer in terms of precision and accuracy, while the MobileNetV2-based model could detect slightly more COVID-19 cases than Xception, and showed slightly fewer false negatives, while giving far more false positives than the other models. Also, the custom CNN model exceeds the MobileNetV2 model in terms of precision. The best accuracy, precision, recall, and F1 score out of these three models were 94.2%, 99%, 95%, and 97%, respectively, as shown by the Xception model. Finally, it was found that the overall accuracy in the current evaluation was curtailed by approximately 2% compared with the average accuracy of previous work on multi-class classification, while a very high precision value was observed, which is of high scientific value.
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