Automated Detection of COVID-19 Cases on Radiographs using Shape-Dependent Fibonacci-p Patterns

Panetta, Karen; Sanghavi, Foram M.; Agaian, Sos S.; Madan, Neel

doi:10.1109/jbhi.2021.3069798

Cited by 29 publications

(27 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Panetta et. al [44] presented a shapedependent Fibonacci -p patterns-based feature extractor for distilling out the intricate textural features from CXR images.…”

Section: Literature Surveymentioning

confidence: 99%

Choquet Integral and Coalition Game-Based Ensemble of Deep Learning Models for COVID-19 Screening From Chest X-Ray Images

Bhowal

Sen

Lee

et al. 2021

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

Under the present circumstances, when we are still under the threat of different strains of coronavirus, and since the most widely used method for COVID-19 detection, RT-PCR is a tedious and time-consuming manual procedure with poor precision, the application of Artificial Intelligence (AI) and Computer-Aided Diagnosis (CAD) is inevitable. Though, some vaccines have now been authorized worldwide, it will take huge time to reach everyone, especially in developing countries. In this work, we have analyzed Chest X-ray (CXR) images for the detection of the coronavirus. The primary agenda of this proposed research study is to leverage the classification performance of the deep learning models using ensemble learning. Many papers have proposed different ensemble learning techniques in this field, some methods using aggregation functions like Weighted Arithmetic Mean (WAM) among others. However, none of these methods take into consideration the decisions that subsets of the classifiers take. In this paper, we have applied Choquet integral for ensemble and propose a novel method for the evaluation of fuzzy measures using coalition game theory, information theory, and Lambda fuzzy approximation. Three different sets of fuzzy measures are calculated using three different weighting schemes along with information theory and coalition game theory. Using these three sets of fuzzy measures, three Choquet integrals are calculated and their decisions are finally combined. Besides, we have created a database by combining several image repositories developed recently. Impressive results on the newly developed dataset and the challenging COVIDx dataset support the efficacy and robustness of the proposed method. Our experimental results outperform many recently proposed methods. Source code of this work is available at https://github.com/ subhankar01/Covid-Chestxray-lambda-fuzzy

show abstract

“…Panetta et. al [44] presented a shapedependent Fibonacci -p patterns-based feature extractor for distilling out the intricate textural features from CXR images.…”

Section: Literature Surveymentioning

confidence: 99%

Choquet Integral and Coalition Game-Based Ensemble of Deep Learning Models for COVID-19 Screening From Chest X-Ray Images

Bhowal

Sen

Lee

et al. 2021

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

show abstract

“…CHP-Net [38] consists of three networks: a bounding box regression network to extract bi-pulmonary region coordinates, a discriminator deep learning model to predict a differentiating probability distribution, and a localization deep network that represents all potential pulmonary locations. In [10] the authors propose using shape dependent Fibonacci p patterns to extract features from chest X-ray images and then apply conventional machine learning algorithms. [39] first extracts orthogonal moment features using Fractional Multichannel Exponent Moments (FrMEMs).…”

Section: B Covid-19 Detection Using Chest X-raymentioning

confidence: 99%

“…Motivated by the success of the Deep Learning in diagnosing respiratory disorders [5] , several recent works have proposed the use of chest radiography images (X-ray and Computed Tomography, CT) as alternate modality to detect COVID-19 positive cases [6] – [12] (Elaborated in Sec. II ).…”

Section: Introductionmentioning

confidence: 99%

xViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography

Mondal

Bhattacharjee

Singla

et al. 2022

IEEE J. Transl. Eng. Health Med.

View full text Add to dashboard Cite

Objective: Since its outbreak, the rapid spread of COrona VIrus Disease 2019 (COVID-19) across the globe has pushed the health care system in many countries to the verge of collapse. Therefore, it is imperative to correctly identify COVID-19 positive patients and isolate them as soon as possible to contain the spread of the disease and reduce the ongoing burden on the healthcare system. The primary COVID-19 screening test, RT-PCR although accurate and reliable, has a long turn-around time. In the recent past, several researchers have demonstrated the use of Deep Learning (DL) methods on chest radiography (such as X-ray and CT) for COVID-19 detection. However, existing CNN based DL methods fail to capture the global context due to their inherent image-specific inductive bias. Methods: Motivated by this, in this work, we propose the use of vision transformers (instead of convolutional networks) for COVID-19 screening using the X-ray and CT images. We employ a multi-stage transfer learning technique to address the issue of data scarcity. Furthermore, we show that the features learned by our transformer networks are explainable. Results: We demonstrate that our method not only quantitatively outperforms the recent benchmarks but also focuses on meaningful regions in the images for detection (as confirmed by Radiologists), aiding not only in accurate diagnosis of COVID-19 but also in localization of the infected area. The code for our implementation can be found here - . Conclusion: The proposed method will help in timely identification of COVID-19 and efficient utilization of limited resources.

show abstract

“…However, since techniques like GA and Particle Swarm Optimization (PSO) suffer from the limitation of poor and pre-mature convergence issues, the authors employed a novel meta-heuristic algorithm, Multi-objective Adaptive Differential Evolution (MADE) for overcoming the convergence issue and for hyperparameter tuning. Panetta et al (2021) presented a shape-dependent Fibonacci -p patterns-based feature extractor for distilling out the intricate textural features from CXR images. This methodology has the inherent advantage of being computationally inexpensive and is tolerant to noise and illumination present in the CXR images.…”

Section: Related Workmentioning

confidence: 99%

A two-tier feature selection method using Coalition game and Nystrom sampling for screening COVID-19 from chest X-Ray images

Bhowal

Sen

Sarkar

2021

J Ambient Intell Human Comput

View full text Add to dashboard Cite

The world is still under the threat of different strains of the coronavirus and the pandemic situation is far from over. The method, that is widely used for the detection of COVID-19 is Reverse Transcription Polymerase chain reaction (RT-PCR), which is a time-consuming method and is prone to manual errors, and has poor precision. Although many nations across the globe have begun the mass immunization procedure, the COVID-19 vaccine will take a long time to reach everyone. The application of artificial intelligence (AI) and computer-aided diagnosis (CAD) has been used in the domain of medical imaging for a long period. It is quite evident that the use of CAD in the detection of COVID-19 is inevitable. The main objective of this paper is to use convolutional neural network (CNN) and a novel feature selection technique to analyze Chest X-Ray (CXR) images for the detection of COVID-19. We propose a novel two-tier feature selection method, which increases the accuracy of the overall classification model used for screening COVID-19 CXRs. Filter feature selection models are often more effective than wrapper methods as wrapper methods tend to be computationally more expensive and are not useful for large datasets dealing with a large number of features. However, most filter methods do not take into consideration how a group of features would work together, rather they just look at the features individually and decide on a score. We have used approximate Shapley value, a concept of Coalition game theory, to deal with this problem. Further, in the case of a large dataset, it is important to work with shorter embeddings of the features. We have used CUR decomposition and Nystrom sampling to further reduce the feature space. To check the efficacy of this two-tier feature selection method, we have applied it to the features extracted by three standard deep learning models, namely VGG16 , Xception and InceptionV3 , where the features have been extracted from the CXR images of COVID-19 datasets and we have found that the selection procedure works quite well for the features extracted by Xception and InceptionV3 . The source code of this work is available at https://github.com/subhankar01/covidfs-aihc .

show abstract

Automated Detection of COVID-19 Cases on Radiographs using Shape-Dependent Fibonacci-p Patterns

Cited by 29 publications

References 43 publications

Choquet Integral and Coalition Game-Based Ensemble of Deep Learning Models for COVID-19 Screening From Chest X-Ray Images

Choquet Integral and Coalition Game-Based Ensemble of Deep Learning Models for COVID-19 Screening From Chest X-Ray Images

xViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography

A two-tier feature selection method using Coalition game and Nystrom sampling for screening COVID-19 from chest X-Ray images

Contact Info

Product

Resources

About