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One-shot Cluster-Based Approach for the Detection of COVID-19 from Chest X-ray Images

V N Manjunath Aradhya, Mufti Mahmud, D S Guru, Basant Agarwal, M Shamim Kaiser
2021 Cognitive Computation  
Chest X-rays are the widely available modalities for immediate diagnosis of COVID-19.  ...  A model for detecting COVID-19 from chest X-ray images is proposed in this paper. A novel concept of cluster-based one-shot learning is introduced in this work.  ...  In this work, we propose a model for detecting COVID-19 chest X-ray images, by introducing a new concept of one-shot clusterbased learning, which has greater advantages of learning from a few samples over  ... 
doi:10.1007/s12559-020-09774-w pmid:33680210 pmcid:PMC7921614 fatcat:dlbs76alxrbl3p6hbd4pnz5juq

Advancement of Deep Learning in Pneumonia and Covid-19 Classification and Localization: A Qualitative and Quantitative Analysis [article]

Aakash Shah, Manan Shah
2021 arXiv   pre-print
With the help of Deep Learning models, pneumonia and covid-19 can be detected instantly from Chest X-rays or CT scans.  ...  , viral pneumonia, and covid-19 from images of chest X-rays and CT scans.  ...  with deep learning", "covid-19 localization with deep learning", "pneumonia detection with Chest X-rays", "pneumonia localization with chest X-rays", "covid-19 detection with chest X-rays", "covid-19  ... 
arXiv:2111.08606v1 fatcat:oqjgceqhtfaghkhojmsoqwzwrq

Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images [article]

Soumick Chatterjee, Fatima Saad, Chompunuch Sarasaen, Suhita Ghosh, Rupali Khatun, Petia Radeva, Georg Rose, Sebastian Stober, Oliver Speck, Andreas Nürnberger
2020 arXiv   pre-print
and healthy subjects using Chest X-Ray.  ...  Medical imaging such as X-ray and Computed Tomography (CT) combined with the potential of Artificial Intelligence (AI) plays an essential role in supporting the medical staff in the diagnosis process.  ...  Training techniques like few-shot learning (including oneshot learning), semi-supervised learning, etc. can be explored for learning to classify COVID-19 cases from a small dataset.  ... 
arXiv:2006.02570v2 fatcat:qsl2ef5mubekvgesg2jd7eieme

IoT-enabled stacked ensemble of deep neural networks for the diagnosis of COVID-19 using chest CT scans

Mohammad Shorfuzzaman
2021 Computing  
This study presents an IoT-enabled deep learning-based stacking model to analyze chest CT scans for effective diagnosis of COVID-19 encounters.  ...  With the aid of various AI functionalities and advanced technologies, chest CT scans may thus be a viable alternative for quick and automatic screening of COVID-19.  ...  A few other studies have also utilized chest X-Ray for the detection of COVID-19 utilizing deep learning approaches. In one of the earliest open-source efforts, Wang et al.  ... 
doi:10.1007/s00607-021-00971-5 fatcat:2lf34gczfnashkdiyblcewumcu

Hybrid Deep Learning for Detecting Lung Diseases from X-ray Images [article]

Subrato Bharati, Prajoy Podder, M. Rubaiyat Hossain Mondal
2020 arXiv   pre-print
The new model is applied to NIH chest X-ray image dataset collected from Kaggle repository. Full and sample versions of the dataset are considered.  ...  Timely diagnosis of lung disease is essential. Many image processing and machine learning models have been developed for this purpose.  ...  In [24] , a framework for deep learning is proposed to predict lung cancer and pneumonia offering two deep learning methods. Initially they use modified AlexNet for diagnosis of chest X-ray.  ... 
arXiv:2003.00682v3 fatcat:fanebp4paregtgg2kgppb34ggy

Hybrid deep learning for detecting lung diseases from X-ray images

Subrato Bharati, Prajoy Podder, M. Rubaiyat Hossain Mondal
2020 Informatics in Medicine Unlocked  
The new model is applied to NIH chest X-ray image dataset collected from Kaggle repository. Full and sample versions of the dataset are considered.  ...  Timely diagnosis of lung disease is essential. Many image processing and machine learning models have been developed for this purpose.  ...  Acknowledgement The authors would like to thank National Institutes of Health (NIH) for uploading their datasets in Kaggle repository.  ... 
doi:10.1016/j.imu.2020.100391 pmid:32835077 pmcid:PMC7341954 fatcat:n4mxxe3k3be4vlulf7jkzsug5q

Retrieval From and Understanding of Large-Scale Multi-modal Medical Datasets: A Review

Henning Muller, Devrim Unay
2017 IEEE transactions on multimedia  
Annotated data sets and clinical data for the images have now become available and can be combined for multimodal retrieval. Much has been learned on user behavior and application scenarios.  ...  In the medical domain visual retrieval started later and has mostly remained a research instrument and less a clinical tool, even though a few tools for retrieval are employed in clinical work.  ...  [42] exploited deep learning features for PET retrieval based Alzheimer's diagnosis, while [38] , [51] employed it for pathology retrieval of chest X-rays.  ... 
doi:10.1109/tmm.2017.2729400 fatcat:td4s7hbegzbmhlosalzlc3p7tq

IEEE Access Special Section Editorial: Emerging Deep Learning Theories and Methods for Biomedical Engineering

Yu-Dong Zhang, Zhengchao Dong, Juan Manuel Gorriz, Yizhang Jiang, Ming Yang, Shui-Hua Wang
2021 IEEE Access  
She has been serving as the Director for the Children's Hospital, Nanjing Medical University, Nanjing, since 2016.  ...  She has served as a Visiting Scholar for the Department of Radiology, University of Maryland from January 2013 to June 2014.  ...  The article ''2019 novel coronavirus-infected pneumonia on CT: A feasibility study of few-shot learning for computerized diagnosis of emergency diseases,'' by Lai et al., develops a machine learning approach  ... 
doi:10.1109/access.2021.3080355 fatcat:oez6u3npt5ff7aw7tscwyvlmvq

Unsupervised Anomaly Detection for X-Ray Images [article]

Diana Davletshina, Valentyn Melnychuk, Viet Tran, Hitansh Singla, Max Berrendorf, Evgeniy Faerman, Michael Fromm, Matthias Schubert
2020 arXiv   pre-print
can be used to assist doctors in evaluating X-ray images of hands.  ...  Therefore, we adopt state-of-the-art approaches for unsupervised learning to detect anomalies and show how the outputs of these methods can be explained.  ...  The authors of this work take full responsibilities for its content.  ... 
arXiv:2001.10883v2 fatcat:lmzd72l3grclfisp6wgkk54sx4

Deformation and Refined Features Based Lesion Detection on Chest X-ray

Ce Li, Dong Zhang, Shaoyi Du, Zhiqiang Tian
2020 IEEE Access  
Automatic and accurate detection of chest X-ray lesion is a challenging task.  ...  In the chest X-ray image, the lesions are shown with blurred boundary contours, different sizes, variable shapes, uneven density, etc.  ...  The transfer learning method provides some theoretical support for the few-shot learning tasks [20] , [38] .  ... 
doi:10.1109/access.2020.2963926 fatcat:fqbsoknsbbefpj5yv2fuwguxq4

On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification [article]

Mauricio Mendez-Ruiz and Francisco Lopez-Tiro and Jonathan El-Beze and Vincent Estrade and Gilberto Ochoa-Ruiz1 and Jacques Hubert and Andres Mendez-Vazquez and Christian Daul
2022 arXiv   pre-print
In this work, we propose the use of a meta-learning based few-shot learning approach to alleviate these problems.  ...  The results show how such methods are indeed capable of handling domain-shifts by attaining an accuracy of 74.38% and 88.52% in the 5-way 5-shot and 5-way 20-shot settings respectively.  ...  ACKNOWLEDGMENTS The authors wish to thank the AI Hub and the CIIOT at ITESM for their support for carrying the experiments reported in this paper in their NVIDIA's DGX computer.  ... 
arXiv:2205.00895v1 fatcat:vj6l3velyjd6xkquscni63thpe

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models [article]

Jialin Peng, Ye Wang
2021 arXiv   pre-print
Despite the remarkable performance of deep learning methods on various tasks, most cutting-edge models rely heavily on large-scale annotated training examples, which are often unavailable for clinical  ...  Therefore, the strong capability of learning and generalizing from limited supervision, including a limited amount of annotations, sparse annotations, and inaccurate annotations, is crucial for the successful  ...  For chest X-ray segmentation with imperfect labels, Xue et al.  ... 
arXiv:2103.00429v1 fatcat:p44a5e34sre4nasea5kjvva55e

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models

Jialin Peng, Ye Wang
2021 IEEE Access  
Despite the remarkable performance of deep learning methods on various tasks, most cutting-edge models rely heavily on large-scale annotated training examples, which are often unavailable for clinical  ...  Therefore, the strong capability of learning and generalizing from limited supervision, including a limited amount of annotations, sparse annotations, and inaccurate annotations, is crucial for the successful  ...  For chest X-ray segmentation with imperfect labels, Xue et al.  ... 
doi:10.1109/access.2021.3062380 fatcat:r5vsec2yfzcy5nk7wusiftyayu

Medical image analysis based on deep learning approach

Muralikrishna Puttagunta, S. Ravi
2021 Multimedia tools and applications  
Most of the DLA implementations concentrate on the X-ray images, computerized tomography, mammography images, and digital histopathology images.  ...  Medical imaging plays a significant role in different clinical applications such as medical procedures used for early detection, monitoring, diagnosis, and treatment evaluation of various medical conditions  ...  [119] proposed modality-specific ensemble learning for the detection of abnormalities in chest X-rays (CXRs).  ... 
doi:10.1007/s11042-021-10707-4 pmid:33841033 pmcid:PMC8023554 fatcat:cm522go4nbdbnglgzpw4nu7tbi

A survey on generative adversarial networks for imbalance problems in computer vision tasks

Vignesh Sampath, Iñaki Maurtua, Juan José Aguilar Martín, Aitor Gutierrez
2021 Journal of Big Data  
In this paper, we examine the most recent developments of GANs based techniques for addressing imbalance problems in image data.  ...  It is particularly important that GANs can not only be used to generate synthetic images, but also its fascinating adversarial learning idea showed good potential in restoring balance in imbalanced datasets  ...  Acknowledgements The authors would like to thank the anonymous reviewers for their valuable comments and suggestions on the paper.  ... 
doi:10.1186/s40537-021-00414-0 pmid:33552840 pmcid:PMC7845583 fatcat:g3p6hbjuj5c5vbe23ms4g6ed6q
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