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Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT

Liang Sun, Zhanhao Mo, Fuhua Yan, Liming Xia, Fei Shan, Zhongxiang Ding, Bin Song, Wanchun Gao, Wei Shao, Feng Shi, Huan Yuan, Huiting Jiang (+6 others)
2020 IEEE journal of biomedical and health informatics  
In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images.  ...  Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-  ...  CONCLUSION In this paper, we propose an adaptive feature selection guided deep forest for COVID-19 vs. CAP classification by using the chest CT images.  ... 
doi:10.1109/jbhi.2020.3019505 pmid:32845849 fatcat:pwerqbb57jbj3do6cbw4bmdvxe

Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT [article]

Liang Sun, Zhanhao Mo, Fuhua Yan, Liming Xia, Fei Shan, Zhongxiang Ding, Wei Shao, Feng Shi, Huan Yuan, Huiting Jiang, Dijia Wu, Ying Wei (+5 others)
2020 arXiv   pre-print
In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images.  ...  Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-  ...  CONCLUSION In this paper, we propose an adaptive feature selection guided deep forest for COVID-19 vs. CAP classification by using the chest CT images.  ... 
arXiv:2005.03264v1 fatcat:7gshtuetevcvlouetxd4a3gg2y

A Survey on Machine Learning in COVID-19 Diagnosis

Xing Guo, Yu-Dong Zhang, Siyuan Lu, Zhihai Lu
2022 CMES - Computer Modeling in Engineering & Sciences  
First, the procedure of the diagnosis based on machine learning is introduced in detail, which includes medical data collection, image preprocessing, feature extraction, and image classification.  ...  Aiming at these problems, we propose some solutions and provide a comprehensive presentation for future research.  ...  DenseNet121, MobileNetV2, majority voting method Sun CT Adaptive feature selection The model based on adaptive feature selection et al. [78] guided deep forest guided deep forest obtained a high-level  ... 
doi:10.32604/cmes.2022.017679 fatcat:hre5zxtekvaevleu335faqilwu

Machine learning for medical imaging‐based COVID‐19 detection and diagnosis

Rokaya Rehouma, Michael Buchert, Yi‐Ping Phoebe Chen
2021 International Journal of Intelligent Systems  
features of medical imaging in patients with COVID-19.  ...  Deep Learning algorithms, particularly convolutional neural networks, have been utilized widely for image segmentation and classification to identify patients with COVID-19 and many ML modules have achieved  ...  Sun et al. 47 used adaptive feature selection guided deep forest (AFS-DF) based on extracted location-specific features from segmented images.  ... 
doi:10.1002/int.22504 fatcat:chzex7hffbfmvfbmruxyf63lvq

2021 Index IEEE Transactions on Artificial Intelligence Vol. 2

2021 IEEE Transactions on Artificial Intelligence  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  Architecture for Improved Lesion Segmentation of COVID-19 Chest CT Scans.  ...  ., +, TAI June 2021 238-250 Segmentation of COVID-19 Chest CT Scans.  ... 
doi:10.1109/tai.2021.3138017 fatcat:ia5kk2h32zglzap5ngyajxhtay

Automatic Detection of Coronavirus (COVID-19) from Chest CT Images using VGG16-Based Deep-Learning

Abolfazl Karimiyan Abdar, Seyyed Mostafa Sadjadi, Hamid Soltanian-Zadeh, Ali Bashirgonbadi, Mehran Naghibi
2020 2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME)  
In this paper, we present a deep learning-based Convolutional Neural Network (CNN) model we developed for the classification of COVID-19 positive patients from healthy subjects using chest CT.  ...  We used 10979 chest CT images of 131 patients with COVID-19 and 150 healthy subjects for training, validating, and testing of the proposed model.  ...  In [10] , Sun, et al. proposed an adaptive feature selection guided deep forest (AFSDF) algorithm for the classification of COVID-19 from chest CT images using a dataset with 1495 patients of COVID-19  ... 
doi:10.1109/icbme51989.2020.9319326 fatcat:kph25tqzczgcvnlnjcafzhh6v4

Prediction Model for Coronavirus Pandemic Using Deep Learning

Mamoona Humayun, Ahmed Alsayat
2022 Computer systems science and engineering  
CT scans and allowing clinicians to accurately discriminate between patients who are sick with COVID-19 or pneumonia, and also empowering health professionals to distinguish chest CT scans of healthy  ...  The efforts done by the Saudi government for the management and control of COVID-19 are remarkable, however; there is a need to improve the diagnostics process for better perception.  ...  The paper [22] suggests an Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification based on Chest CT images (AFS-DF).  ... 
doi:10.32604/csse.2022.019288 fatcat:6i3nx2y2bnfivnjed22t3b5ay4

A Survey of the Application of Artifical Intellegence on COVID-19 Diagnosis and Prediction

H. Alalawi, M. Alsuwat, H. Alhakami
2021 Engineering, Technology & Applied Science Research  
Additionally, with Coronavirus (COVID-19) propagation since 2019, the world still faces a great challenge in defeating COVID-19 even with modern methods and technologies.  ...  COVID-19 diagnosis and detection.  ...  Implemented a personalized network (DarkCovidNet) for automatic COVID-19 detection A fully automated chest CT framing for detecting COVID-19 Proposed a method for binary classification and multi-classification  ... 
doi:10.48084/etasr.4503 fatcat:fgjbemqcbzcatn542z2skqwfva

An Automated Early Detection and Classification Method for COVID-19 Stages based on deep learning technique using chest CT images.(Dept.E)

Mohamed Abdelsalam, Mervat El-Seddek
2021 MEJ. Mansoura Engineering Journal  
In [25] , they used An Adaptive Feature Selection guided Deep Forest (AFS-DF) to classify COVID-19 patients based on using chest CT images.  ...  A deep learning-based system for chest CT images automatic segmentation [19] .  ... 
doi:10.21608/bfemu.2021.169739 fatcat:d6fuh2uo2fg2zkt5scvqd2avyu

Detection of COVID-19 from CT scan images: A spiking neural network-based approach

Avishek Garain, Arpan Basu, Fabio Giampaolo, Juan D. Velasquez, Ram Sarkar
2021 Neural computing & applications (Print)  
We have applied them for the classification of chest CT scan images into COVID and non-COVID classes.  ...  Desperate times have called for desperate measures to detect the disease at an early stage via various medically proven methods like chest computed tomography (CT) scan, chest X-Ray, etc., in order to  ...  Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long  ... 
doi:10.1007/s00521-021-05910-1 pmid:33879976 pmcid:PMC8050640 fatcat:5c6usqvzebec5j7n3m3gtspn7u

Deep learning based diagnosis of COVID-19 using chest CT-scan images

Talha Anwar, Seemab Zakir
2020 2020 IEEE 23rd International Multitopic Conference (INMIC)  
In this paper, deep learning technology is used to diagnose COVID-19 in subjects through chest CT-scan.  ...  EfficientNet deep learning architecture is used for timely and accurate detection of coronavirus with an accuracy 0.897, F1 score 0.896, and AUC 0.895.  ...  [15] proposed Adaptive Feature Selection guided Deep Forest algorithm for this purpose. They used 1495 CT-scan images of COVID patients and 1027 images of CAP cases.  ... 
doi:10.1109/inmic50486.2020.9318212 fatcat:74blmk3a7jf3lfognkfev2izei

The diagnostic accuracy of Artificial Intelligence-Assisted CT imaging in COVID-19 disease: A systematic review and meta-analysis

Meisam Moezzi, Kiarash Shirbandi, Hassan Kiani Shahvandi, Babak Arjmand, Fakher Rahim
2021 Informatics in Medicine Unlocked  
This systematic review summarizes all the data currently available on the AI-assisted CT-Scan prediction accuracy for COVID-19.  ...  More prospective real-time trials are required to confirm AI's role for high and quick COVID-19 diagnosis due to the possible selection bias and retrospective existence of currently available studies.  ...  Liang Sun et al., 2020 [10] DL Chest CT scans The lung segmentation and severity assessment of COVID19 patients VB-Net - Adaptive Feature Selection guided Deep Forest (AFS-DF) - Selection  ... 
doi:10.1016/j.imu.2021.100591 pmid:33977119 pmcid:PMC8099790 fatcat:mluhgxfhdrch7p537vwqrmgxpq

Ensemble Deep Learning and Internet of Things-Based Automated COVID-19 Diagnosis Framework

Anita S. Kini, A. Nanda Gopal Reddy, Manjit Kaur, S. Satheesh, Jagendra Singh, Thomas Martinetz, Hammam Alshazly, Yuvaraja Teekaraman
2022 Contrast Media & Molecular Imaging  
The modalities such as computed tomography (CT) have been successfully utilized for the early stage diagnosis of COVID-19 infected patients.  ...  Recently, many researchers have utilized deep learning models for the automated screening of COVID-19 suspected cases.  ...  Adaptive feature selection guided deep forest (AGGDF) was designed for the diagnosis of COVID-19 suspected case [24] . e genetic CNN (GCNN) was implemented. e GCNN was trained from scratch to extract  ... 
doi:10.1155/2022/7377502 pmid:35280708 pmcid:PMC8896964 fatcat:pkwimsq3cbhkzd7vjlbr665wee

COVID-19 and Pneumonia Prognosis System using DWT based Feature Extraction, Machine Learning and ANN

Parth Somkuwar, Smt. V Rama
2021 2021 International Conference on Intelligent Technologies (CONIT)  
It includes approximately. 5000 Normal, Covid-19, and Pneumonia X-ray pics for each class.  ...  With the aid of waveletbased image segmentation, feature extraction strategies, knowledge of machine learning and neural networks one such system may be produced.  ...  Researchers [5] gift COVID-Net, a deep convolutional community targeted on Chest Xray pics for COVID-19 prognosis.  ... 
doi:10.1109/conit51480.2021.9498369 fatcat:lgzti2tcebhilin5era2ijqtm4

Optimize Edilmiş ÇKA ile Covıd-19 Sınıflandırması için Kaynaştırılmış Derin Özelliklere Dayalı Sınıflandırma Çerçevesi

Şaban ÖZTÜRK, Enes YİĞİT, Umut ÖZKAYA
2020 Selcuk University Journal of Engineering Science and Technology  
These new image features matrices obtained with feature fusion are classified for COVID detection.  ...  First, COVID-19 images are trained using ResNet-50 and VGG-16 architectures. Then, features in the last layer of these two architectures are combined with feature fusion.  ...  Sun et al. (2020) proposed an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. The results produced by hand-crafted methods are inspiring.  ... 
doi:10.36306/konjes.821782 fatcat:apxlg4arsrdg3dpkcqtnmjawke
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