Transfer learning based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data

Singh, Mukul; Bansal, Shrey; Ahuja, Sakshi; Dubey, Rahul; Panigrahi, Bijaya Ketan; Dey, Nilanjan

doi:10.21203/rs.3.rs-32493/v1

Cited by 14 publications

(16 citation statements)

References 13 publications

(13 reference statements)

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“…While the number of parameters would reduce, it might be possible to achieve good performance from a small number of COVID-19 cases [137] , [138] . Ensemble learning [139] , [140] , and multi-task learning [112] , [141] are better suited for COVID-19 diagnosis in the context of a small number of data. In the case of ensemble learning, multiple architectures are developed instead of a single network and finally, the results of each network are combined.…”

Section: Open Discussion Challenges and Future Trendsmentioning

confidence: 99%

A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

et al. 2021

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Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence rate of COVID-19 is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray. This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks. It also highlights the data partitioning techniques and various performance measures developed by researchers in this field. A taxonomy is drawn to categorize the recent works for proper insight. Finally, we conclude by addressing the challenges associated with the use of deep learning methods for COVID-19 detection and probable future trends in this research area. The aim of this paper is to facilitate experts (medical or otherwise) and technicians in understanding the ways deep learning techniques are used in this regard and how they can be potentially further utilized to combat the outbreak of COVID-19.

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Section: Open Discussion Challenges and Future Trendsmentioning

confidence: 99%

A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

et al. 2021

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“…Singh et al [ 32 ] used VGG16, a deep transfer learning architecture, to detect COVID-19 from CT scans. The extracted features were selected using principal component analysis (PCA) and classified by four different classifiers.…”

Section: Related Workmentioning

confidence: 99%

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

Islam

Asraf

2020

Informatics in Medicine Unlocked

532

278

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Nowadays, automatic disease detection has become a crucial issue in medical science due to rapid population growth. An automatic disease detection framework assists doctors in the diagnosis of disease and provides exact, consistent, and fast results and reduces the death rate. Coronavirus (COVID-19) has become one of the most severe and acute diseases in recent times and has spread globally. Therefore, an automated detection system, as the fastest diagnostic option, should be implemented to impede COVID-19 from spreading. This paper aims to introduce a deep learning technique based on the combination of a convolutional neural network (CNN) and long short-term memory (LSTM) to diagnose COVID-19 automatically from X-ray images. In this system, CNN is used for deep feature extraction and LSTM is used for detection using the extracted feature. A collection of 4575 X-ray images, including 1525 images of COVID-19, were used as a dataset in this system. The experimental results show that our proposed system achieved an accuracy of 99.4%, AUC of 99.9%, specificity of 99.2%, sensitivity of 99.3%, and F1-score of 98.9%. The system achieved desired results on the currently available dataset, which can be further improved when more COVID-19 images become available. The proposed system can help doctors to diagnose and treat COVID-19 patients easily.

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“…As AI researchers have a short period to find true solutions for early detection and diagnosis, the PTM with TL feature is the best solution to fit this scenario. The AI studies that more effectively used PTM with TL: AlexNet, [172][173][174][175] VGG, 172,[175][176][177][178][179][180][181][182] ResNet, 173,[175][176][177][181][182][183][184][185][186][187][188] GoogleNet, 173,[176][177][178]181,182,188 DenseNet, 182,183,188 MobileNet, 178,185,189 and SqueezeNet. 183,184,189,190…”

Section: Covid-19mentioning

confidence: 99%

Convolutional neural network and its pretrained models for image classification and object detection: A survey

Jena

Nayak

Saxena

2021

Concurrency and Computation

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At present, in the age of computers and automation of services, deep learning (DL) technology, mainly the subset of machine learning (ML) and artificial intelligence (AI), is expressively used in innumerable domains of computer vision such as data analysis, image recognition, classification, natural language processing, and many more. It has become the foremost choice of researchers as of its effectiveness in producing decent results. This paper presents detailed and analytical literature starting from the very elementary level to the recent trends of this trending technology while focusing on the most used DL model, that is, convolutional neural network and its pretrained models for image classification and object detection. It also reviews diverse existing current literature based on this. Further, a brief introduction of AI, ML, and DL has also been presented, making the foundation for the readers. As pretrained models continuously give an upper edge to DL over ML and other technologies, 23 most popular pretrained models with their architectural diagrams have also been presented. This paper aims to summarize and analyze all the concepts used to formulate DL and its models. Also, we have emphasized more on the GoogleNet models and the entire Inception modules in detail. Finally, the fascinating applications and discussion on integral components of DL have been presented. This paper will definitely draw the attention of the students and researchers working in the area of DL and its models.

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Transfer learning based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data

Cited by 14 publications

References 13 publications

A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

Convolutional neural network and its pretrained models for image classification and object detection: A survey

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