Forecasting COVID-19 via Registration Slips of Patients using ResNet-101 and Performance Analysis and Comparison of Prediction for COVID-19 using Faster R-CNN, Mask R-CNN, and ResNet-50

Tahir, Hassam; Iftikhar, Annas; Mumraiz, Mustehsan

doi:10.1109/icaect49130.2021.9392487

Cited by 30 publications

(10 citation statements)

References 9 publications

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“…H. Tahir et.al [25], proposed a neural network for Comparison of Prediction for Covid-19 using Faster RCNN mask and RestNet-101 and obtained an accuracy of 82%. Since the CNN is a larger model thus having more trainable parameters increases the computational complexity of the model.…”

Section: Results Of Proposed Workmentioning

confidence: 99%

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Investigation and Implementation of Convolutional Neural Networks with Transfer Learning for Detection of Covid-19

Raghavendra

Sriram

Kumar

et al. 2022

J. Phys.: Conf. Ser.

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This paper aims to present a robust model which uses Artificial Intelligence in the rapid and accurate detection of Covid-19. The Proposed Project uses Deep Learning techniques for the detection of Covid-19 as well as Pneumonia with the help of Digitalized Chest X-rays. By Applying Pre-Trained Networks which are also known as Convolutional Neural Networks, this can be made possible. Also, with the help of Transfer Learning Techniques the Neural Networks can be trained and validated faster and better thus providing a significantly higher chance in the correct and accurate detection. The CNNs taken into consideration are VGG-19 and ResNet-50 with the former providing an accuracy of over 95% and the latter with an accuracy of 92% proving that such a high accuracy computer-aided diagnostic tool can be used at a time like this. Also, further improvements in the future with advancements in technology can provide even astonishing results.

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Section: Results Of Proposed Workmentioning

confidence: 99%

“…The metrics shown in figure 6 gives a general understanding of how well the model is trained for the dataset provided and thus having a better Precision and F1 Score indicates that the model can make much more accurate predictions. [22] ResNet-50 with SVM 95 --95 [23] CoroDet 94 94 92 91 [24] ResNet-101 82 89 87 - [25] VGG-19 68 ---…”

Section: Workingmentioning

confidence: 99%

Investigation and Implementation of Convolutional Neural Networks with Transfer Learning for Detection of Covid-19

Raghavendra

Sriram

Kumar

et al. 2022

J. Phys.: Conf. Ser.

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“…Different approaches used Faster-RCNN as a part of a model, such as [34] who used it for lung isolation before the classification process. Also [31] forecast-ed COVID-19 applying Faster-RCNN and other models but included registration slips along with the chest X-rays. [30] adopted Faster-RCNN but integrated it with another networks and models to improve its performance achieving mAP of 39.23%.…”

Section: Discussionmentioning

confidence: 99%

“…Emphasizing on covid-19 detection, Tahir et al [31] use Faster-RCNN to forecast covid-19 through chest X-rays and registration slips of admitted patients and achieves 87% mean Average Precision (mAP). Another approach is by Podder et al [32] who use Mask-RCNN rather than Faster-RCNN to detect COVID-19 through frontal views of chest X-rays achieving 96.98% accuracy.…”

Section: Medical-images Usage: Pneumonia/covid-19 Emphasismentioning

confidence: 99%

Pneumonia and COVID-19 Classification in Chest X-rays Using Faster Region-Based Convolutional Neural Networks (Faster R-CNN)

Farhat

Sakr²,

Kilany

et al. 2022

Preprint

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Chest X-rays have been a subject of deep learning applications on medical images forthe high expectations of their evidence in assessing pulmonary diseases. Whereas thearising of COVID-19 or 2019 novel coronavirus in December 2019 have prioritizedresearch on pulmonary diseases diagnosis and prognosis, especially using artificialintelligence (AI) and Deep Learning (DL). For this sake, we extend the work ondetecting pneumonia using Faster Region-Based Convolutional Neural Networks(Faster R-CNN) by applying Faster R-CNN to the detection of Pneumonia and COVID-19 in Chest X-ray images using several datasets involving COVID-19 images. Differentcombinations of training scenarios in addition to internal and external testing atdifferent objectiveness thresholds, epochs counts, and lengths yielded variant results.Our results comply with the state of the art of Faster-RCNN in pneumonia detection butdo not show promising results in COVID-19 detection as a standalone model. Futurework may emphasize introducing segmentation to the model pipeline, adding asecondary classification stage, and even engaging other medical data to improve theperformance and constitute a robust Faster R-CNN-based prediction model.

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“…In-house dataset CXR-95.5% (CXR) COVID19 (1000 images), CXR-NonCOVID19 (1000 images) [21] Chest X-ray ResNet50 Public 8000 images 93.2% (CXR) [22] Covid CT, X-AlexNet Fusion of several 94% ray datasets (Augmented 3974, Real 1044) [12] X-rays RestNet101 -8009 images 82%…”

Section: Related Workmentioning

confidence: 99%

Readiness Assessment of Public University in Bangladesh for Education 4.0: A Case Study of Shahjalal University of Science and Technology – Sylhet, Bangladesh

Islam

2022

Khulna Univ. Stud.

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In order to get the benefits of Industry 4.0 (4ir) we need the graduates who are able to face the challenges of 4ir. As the universities are the main sources of producing graduates, they should produce adaptable graduates of 4ir. The 4ir demands such a system of higher education where each and every stakeholders especially educators and learners should be connected in a globalized automated environment, networked, virtualized and flexible. In the paradigm of 4ir learners should learn to learn the sources of learning how to build knowledge and skills within the process of teaching –learning. The study aims to identify the dimensions of E4.0 corresponding to 4ir and to assess the readiness of Shahjalal University of Science and Technology (SUST) is one of 50 public universities of Bangladesh. At present there are 28 disciplines in six schools of studies in SUST and among them 20 departments have been selected for the study based on minimum 10 years of establishment. The assessment has been focused on four aspects mainly curriculum, teacher (faculty), intuitional qualification and logistic supports in terms of E4.0. In conducting the study data from collected 120 teachers and graduate students with a structured questionnaire. The questionnaire was five point scales. The collected data was analyzed with SPSS and mainly frequency in each dimension was found and finally factor analysis was done to find the factors affecting E4.0. The frequency distribution showed that Robotic Process Automation aspects ranked first following cloud computing, data analytics, IoT, AI, and BCT. The factor analysis showed that seventeen aspects have been considered as important and the top most five aspects are fees and rewards, data as a service, software as a service, AI extraction and user device.

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Forecasting COVID-19 via Registration Slips of Patients using ResNet-101 and Performance Analysis and Comparison of Prediction for COVID-19 using Faster R-CNN, Mask R-CNN, and ResNet-50

Cited by 30 publications

References 9 publications

Investigation and Implementation of Convolutional Neural Networks with Transfer Learning for Detection of Covid-19

Investigation and Implementation of Convolutional Neural Networks with Transfer Learning for Detection of Covid-19

Pneumonia and COVID-19 Classification in Chest X-rays Using Faster Region-Based Convolutional Neural Networks (Faster R-CNN)

Readiness Assessment of Public University in Bangladesh for Education 4.0: A Case Study of Shahjalal University of Science and Technology – Sylhet, Bangladesh

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