Refine Search

New Search

Results: 7

(searched for: A Review of Recent Deep Learning Models in COVID-19 Diagnosis)
Save to Scifeed
Page of 1
Articles per Page
by
Show export options
  Select all
Ela Bhattacharya, D. Bhattacharya
European Journal of Engineering and Technology Research, Volume 6, pp 10-15; doi:10.24018/ejers.2021.6.5.2485

Abstract:
COVID-19 has emerged as the latest worrisome pandemic, which is reported to have its outbreak in Wuhan, China. The infection spreads by means of human contact, as a result, it has caused massive infections across 200 countries around the world. Artificial intelligence has likewise contributed to managing the COVID-19 pandemic in various aspects within a short span of time. Deep Neural Networks that are explored in this paper have contributed to the detection of COVID-19 from imaging sources. The datasets, pre-processing, segmentation, feature extraction, classification and test results which can be useful for discovering future directions in the domain of automatic diagnosis of the disease, utilizing artificial intelligence-based frameworks, have been investigated in this paper.
Walid Hariri, Ali Narin
Published: 10 December 2020
Abstract:
The new coronavirus disease (COVID-19) has been declared a pandemic since March 2020 by the World Health Organization. It consists of an emerging viral infection with respiratory tropism that could develop atypical pneumonia. Experts emphasize the importance of early detection of those who have the COVID-19 virus. In this way, patients will be isolated from other people and the spread of the virus can be prevented. For this reason, it has become an area of interest to develop early diagnosis and detection methods to ensure a rapid treatment process and prevent the virus from spreading. Since the standard testing system is time-consuming and not available for everyone, alternative early-screening techniques have become an urgent need. In this study, the approaches used in the detection of COVID-19 based on deep learning (DL) algorithms, which have been popular in recent years, have been comprehensively discussed. The advantages and disadvantages of different approaches used in literature are examined in detail. The Computed Tomography of the chest and X-ray images give a rich representation of the patient's lung that is less time-consuming and allows an efficient viral pneumonia detection using the DL algorithms. The first step is the pre-processing of these images to remove noise. Next, deep features are extracted using multiple types of deep models (pre-trained models, generative models, generic neural networks, etc). Finally, the classification is performed using the obtained features to decide whether the patient is infected by coronavirus or it is another lung disease. In this study, we also give a brief review of the latest applications of cough analysis to early screen the COVID-19, and human mobility estimation to limit its spread.
, Mahsa Shahidi
Published: 1 December 2020
Intelligence-Based Medicine, Volume 3-4, pp 100005-100005; doi:10.1016/j.ibmed.2020.100005

Abstract:
The challenge of learning a new concept, object, or a new medical disease recognition without receiving any examples beforehand is called Zero-Shot Learning (ZSL). One of the major issues in deep learning based methodologies such as in Medical Imaging and other real-world applications is the requirement of large annotated datasets prepared by clinicians or experts to train the model. ZSL is known for having minimal human intervention by relying only on previously known or trained concepts plus currently existing auxiliary information. This is ever-growing research for the cases where we have very limited or no annotated datasets available and the detection/recognition system has human-like characteristics in learning new concepts. This makes the ZSL applicable in many real-world scenarios, from unknown object detection in autonomous vehicles to medical imaging and unforeseen diseases such as COVID-19 Chest X-Ray (CXR) based diagnosis. In this review paper, we introduce a novel and broaden solution called Few/one-shot learning, and present the definition of the ZSL problem as an extreme case of the few-shot learning. We review over fundamentals and the challenging steps of Zero-Shot Learning, including state-of-the-art categories of solutions, as well as our recommended solution, motivations behind each approach, their advantages over each category to guide both clinicians and AI researchers to proceed with the best techniques and practices based on their applications. Inspired from different settings and extensions, we then review through different datasets inducing medical and non-medical images, the variety of splits, and the evaluation protocols proposed so far. Finally, we discuss the recent applications and future directions of ZSL. We aim to convey a useful intuition through this paper towards the goal of handling complex learning tasks more similar to the way humans learn. We mainly focus on two applications in the current modern yet challenging era: coping with an early and fast diagnosis of COVID-19 cases, and also encouraging the readers to develop other similar AI-based automated detection/recognition systems using ZSL.
Delong Chen, Shunhui Ji, Fan Liu, Zewen Li, Xinyu Zhou
2020 6th International Conference on Robotics and Artificial Intelligence; doi:10.1145/3449301.3449778

Abstract:
The pandemic of COVID-19 has caused millions of infections, which has led to a great loss all over the world, socially and economically. Due to the false-negative rate and the time-consuming of the conventional Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, diagnosing based on X-ray images and Computed Tomography (CT) images has been widely adopted. Therefore, researchers of the computer vision area have developed many automatic diagnosing models based on machine learning or deep learning to assist the radiologists and improve the diagnosing accuracy. In this paper, we present a review of these recently emerging automatic diagnosing models. 70 models proposed from February 14, 2020, to July 21, 2020, are involved. We analyzed the models from the perspective of preprocessing, feature extraction, classification, and evaluation. Based on the limitation of existing models, we pointed out that domain adaption in transfer learning and interpretability promotion would be the possible future directions.
Mahdi Rezaei, Mahsa Shahidi
Published: 10 June 2020
SSRN Electronic Journal; doi:10.2139/ssrn.3624379

Abstract:
The challenge of learning a new concept, object, or a new medical disease recognition without receiving any examples beforehand is called Zero-Shot Learning (ZSL). One of the major issues in deep learning-based methodologies such as in Medical Imaging, Autonomous Systems and other real-world applications is the requirement of feeding a large annotated and labelled datasets, prepared by an expert human to train the network model. ZSL is known for having minimal human intervention by mainly relying only on previously known concepts and current auxiliary information. This is ever-growing research for the cases where we have very limited or no datasets available and at the same time, the detection/recognition system has human-like characteristics in learning new concepts. Therefore, it makes it applicable in real-world scenarios, from developing autonomous vehicles to medical imaging and COVID-19 Chest X-Ray (CXR) based diagnosis. In this review paper, we present the definition of the problem, we review over fundamentals, and the challenging steps of Zero-Shot Learning, including state-of-the-art categories of solutions as well as our recommended solution, motivations behind each approach, and their advantages over each category to guide the researchers to proceed with the best techniques and practices based on their applications. Inspired from different settings and extensions, we introduce a novel and broaden solution called one$\slash$few-shot learning. We then review through different image datasets inducing medical and non-medical images, the variety of splits, and the evaluation protocols proposed so far. Finally, we discuss the recent applications and future directions of ZSL. We aim to convey a useful intuition through this paper towards the goal of handling complex computer vision learning tasks more similar to the way humans learn. We mainly focus on two applications in the current modern yet challenging era: copping with an early and fast diagnosis of COVID-19 cases in the 2020 pandemic, as well as helping to develop safer autonomous systems, such as self-driving cars and ambulances.
, Mahsa Shahidi
Published: 6 May 2020
Abstract:
The challenge of learning a new concept, object, or a new medical disease recognition without receiving any examples beforehand is called Zero-Shot Learning (ZSL). One of the major issues in deep learning-based methodologies such as in Medical Imaging, Autonomous Systems and other real-world applications is the requirement of feeding a large annotated and labelled datasets, prepared by an expert human to train the network model. ZSL is known for having minimal human intervention by mainly relying only on previously known concepts and current auxiliary information. This is ever-growing research for the cases where we have very limited or no datasets available and at the same time, the detection/recognition system has human-like characteristics in learning new concepts. Therefore, it makes it applicable in real-world scenarios, from developing autonomous vehicles to medical imaging and COVID-19 Chest X-Ray (CXR) based diagnosis. In this review paper, we present the definition of the problem, we review over fundamentals, and the challenging steps of Zero-Shot Learning, including state-of-the-art categories of solutions as well as our recommended solution, motivations behind each approach, and their advantages over each category to guide the researchers to proceed with the best techniques and practices based on their applications. Inspired from different settings and extensions, we introduce a novel and broaden solution called one/few-shot learning. We then review through different image datasets inducing medical and non-medical images, the variety of splits, and the evaluation protocols proposed so far. Finally, we discuss the recent applications and future directions of ZSL. We aim to convey a useful intuition through this paper towards the goal of handling complex computer vision learning tasks more similar to the way humans learn. We mainly focus on two applications in the current modern yet challenging era: copping with an early and fast diagnosis of COVID-19 cases in the 2020 pandemic, as well as helping to develop safer autonomous systems, such as self-driving cars and ambulances.
Mahdi Rezaei, Mahsa Shahidi
Published: 29 April 2020
Abstract:
The challenge of learning a new concept without receiving any examples beforehand is called zero-shot learning (ZSL). One of the major issues in deep learning based methodologies is the requirement of feeding a vast amount of annotated and labelled images by a human to train the network model. ZSL is known for having minimal human intervention by relying only on previously known concepts and auxiliary information. It is an ever-growing research area since it has human-like characteristics in learning new concepts, which makes it applicable in many real-world scenarios, from autonomous vehicles to surveillance systems to medical imaging and COVID-19 CT scan-based diagnosis. In this paper, we present the definition of the problem, we review over fundamentals, and the challenging steps of Zero-shot learning, including recent categories of solutions, motivations behind each approach, and their advantages over other categories. Inspired from different settings and extensions, we have a broaden solution called one/few-shot learning. We then review thorough datasets, the variety of splits, and the evaluation protocols proposed so far. Finally, we discuss the recent applications and possible future directions of ZSL. We aim to convey a useful intuition through this paper towards the goal of handling computer vision learning tasks more similar to the way humans learn.
Page of 1
Articles per Page
by
Show export options
  Select all
Back to Top Top