Analysing most efficient deep learning model to detect  COVID-19 from computer tomography images

Shamrat, F. M. Javed Mehedi; Chakraborty, Sovon; Ahammad, Rasel; Shitab, Tanzil Mahbub; Kazi, Md. Aslam; Hossain, Alamin; Mahmud, Imran

doi:10.11591/ijeecs.v26.i1.pp462-471

Cited by 14 publications

(11 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The random forest (RF) classifier is an ensemble algorithm [11]- [13] which means that it is made up of multiple algorithms. It usually comprises numerous DT algorithms in this scenario [14].…”

Section: Random Forestmentioning

confidence: 99%

A predictive analysis framework of heart disease using machine learning approaches

et al. 2022

Self Cite

View full text Add to dashboard Cite

Heart diseaseis among the leading causes for death globally. Thus, early identification and treatment are indispensable to prevent the disease. In this work, we propose a framework based on machine learning algorithms to tackle such problems through the identification of risk variables associated to this disease. To ensure the success of our proposed model, influential data pre-processing and data transformation strategies are used to generate accurate data for the training model that utilizes the five most popular datasets (Hungarian, Stat log, Switzerland, Long Beach VA, and Cleveland) from UCI. The univariate feature selection technique is applied to identify essential features and during the training phase, classifiers, namely extreme gradient boosting (XGBoost), support vector machine (SVM), random forest (RF), gradient boosting (GB), and decision tree (DT), are deployed. Subsequently, various performance evaluations are measured to demonstrate accurate predictions using the introduced algorithms. The inclusion of Univariate results indicated that the DT classifier achieves a comparatively higher accuracy of around 97.75% than others. Thus, a machine learning approach is recognize, that can predict heart disease with high accuracy. Furthermore, the 10 attributes chosen are used to analyze the model's outcomes explainability, indicating which attributes are more significant in the model's outcome.

show abstract

Section: Random Forestmentioning

confidence: 99%

A predictive analysis framework of heart disease using machine learning approaches

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…We looked at which pre-trained network will fit this dataset the best. Three separate models, VGG16, ResNet50, and Inception-v3 were examined [37]. From Table 5, we can see Inception-v3 performs the most accuracy with 97.86% with less time 485 seconds, ResNet-50 performs the second-highest accuracy with 95.68% with 1090 seconds and lastly, VGG-16 performs 95.48% accuracy taken highest time of 2510 seconds to detect eye diseases.…”

Section: Resultsmentioning

confidence: 99%

Multi categorical of common eye disease detect using convolutional neural network: a transfer learning approach

Bitto

Mahmud

2022

Bulletin EEI

Self Cite

View full text Add to dashboard Cite

Among the most important systems in the body is the eyes. Although their small stature, humans are unable to imagine existence without it. The human optic is safe against dust particles by a narrow layer called the conjunctiva. It prevents friction during the opening and shutting of the eye by acting as a lubricant. A cataract is an opacification of the eye's lens. There are various forms of eye problems. Because the visual system is the most important of the four sensory organs, external eye abnormalities must be detected early. The classification technique can be used in a variety of situations. A few of these uses are in the healthcare profession. We use visual geometry group (VGG-16), ResNet-50, and Inception-v3 architectures of convolutional neural networks (CNNs) to distinguish between normal eyes, conjunctivitis eyes, and cataract eyes throughout this paper. With a detection time of 485 seconds, Inception-v3 is the most accurate at detecting eye disease, with a 97.08% accuracy, ResNet-50 performs the second-highest accuracy with 95.68% with 1090 seconds and lastly, VGG-16 performs 95.48% accuracy taking the highest time of 2510 seconds to detect eye diseases.

show abstract

“…For example, many researchers, with the help of hospitals and doctors, used patient chest x-ray images to apply convolutional neural networks or deep transfer learning algorithms in detecting the virus, with a very high success rate [14]- [16]. Another example is the smart use of chest computer tomography (CT) scan datasets [17], image processing and neural network algorithm. For that, "GraphCovidNet" model is suggested to predict the COVID-19 virus with very promising results [18].…”

Section: Virus Detection and Predictionmentioning

confidence: 99%

Evolution of automated learning techniques for combating COVID-19: an analysis

Mohammed

Hamsheen

2022

IJEECS

View full text Add to dashboard Cite

It is now more than two years that the world is battling the tiny invisible virus, COVID-19. Since its appearance, it showered humankind with shock, fear, and death. In small words, this pandemic has paused human life in all its aspects and beauties. Governments, health industry researchers and laboratories have put all their efforts to achieve a universal goal that is, overcoming the crisis and putting an end to the pandemic. However, this goal was never achievable without the smart use of automated learning, artificial intelligence, machine learning and deep learning algorithms. This review paper presents a collection of the experimental research articles tackled using real-time official datasets from hospitals and governments. These datasets are processed using automated learning (AL) algorithms in order to find suitable solutions to most of the COVID-19 related problems. This paper presents the AL applications in a story telling manner, starting from the first phases of COVID-19, when doctors had no experience dealing with the disease and had difficulty in diagnosing it, then moving to the other phases like suggesting a medicine, drug repurposing, facial mask detection, fake news detection, vaccine development, pandemic management, post vaccine statistics and lastly post COVID-19 analysis.

show abstract

Analysing most efficient deep learning model to detect COVID-19 from computer tomography images

Cited by 14 publications

References 18 publications

A predictive analysis framework of heart disease using machine learning approaches

A predictive analysis framework of heart disease using machine learning approaches

Multi categorical of common eye disease detect using convolutional neural network: a transfer learning approach

Evolution of automated learning techniques for combating COVID-19: an analysis

Contact Info

Product

Resources

About