Advanced Empirical Studies on Group Governance of the Novel Corona Virus, MERS, SARS and EBOLA: A Systematic Study

Bhattacharyya, Debina; Kumari, Nisha; Joshua, Eali Stephen Neal; Rao, Nikhil

doi:10.31782/ijcrr.2020.121828

Cited by 17 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are thoracic CT scans in the Lung Image Database Consortium image collection (LIDC-IDRI) [16] that have been annotated with lesions for the diagnosis and screening of lung cancer that may be used for diagnostic and screening purposes. Each CT image was reviewed by a radiology specialist who categorized the lesions as "nodule > or = 3 mm," "nodule 3 mm," or "nonnodule > or = 3 mm."…”

Section: Image Dataset Description and Data Augmentationmentioning

confidence: 99%

Lung Cancer Segmentation with Three-Parameter Logistic Type Distribution燤odel

Bhattacharyya¹,

Joshua²,

Rao³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Lung cancer is the leading cause of mortality in the world affecting both men and women equally. When a radiologist just focuses on the patient's body, it increases the amount of strain on the radiologist and the likelihood of missing pathological information such as abnormalities are increased. One of the primary objectives of this research work is to develop computerassisted diagnosis and detection of lung cancer. It also intends to make it easier for radiologists to identify and diagnose lung cancer accurately. The proposed strategy which was based on a unique image feature, took into consideration the spatial interaction of voxels that were next to one another. Using the U-NET+Three parameter logistic distribution-based technique, we were able to replicate the situation. The proposed technique had an average Dice co-efficient (DSC) of 97.3%, a sensitivity of 96.5% and a specificity of 94.1% when tested on the Luna-16 dataset. This research investigates how diverse lung segmentation, juxta pleural nodule inclusion, and pulmonary nodule segmentation approaches may be applied to create Computer Aided Diagnosis (CAD) systems. When we compared our approach to four other lung segmentation methods, we discovered that ours was the most successful. We employed 40 patients from Luna-16 datasets to evaluate this. In terms of DSC performance, the findings demonstrate that the suggested technique outperforms the other strategies by a significant margin.

show abstract

Section: Image Dataset Description and Data Augmentationmentioning

confidence: 99%

Lung Cancer Segmentation with Three-Parameter Logistic Type Distribution燤odel

Bhattacharyya¹,

Joshua²,

Rao³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…This type of regression method enables the detection of complex relations between input variables, as well as the automatic detection of interactions between input variables and the output [ 18 ]. Such an approach has shown to be successful in the modeling of previous epidemics, including but not limited to SARS [ 19 ], H1N1 [ 20 , 21 ], avian flu [ 22 , 23 ], AIDS [ 24 , 25 ], and Ebola [ 26 , 27 ]. Thus, it is evident that the AI-based approach may greatly help with the modeling of COVID-19 epidemiological curves.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review

Musulin

Šegota

Štifanić

et al. 2021

IJERPH

View full text Add to dashboard Cite

COVID-19 is one of the greatest challenges humanity has faced recently, forcing a change in the daily lives of billions of people worldwide. Therefore, many efforts have been made by researchers across the globe in the attempt of determining the models of COVID-19 spread. The objectives of this review are to analyze some of the open-access datasets mostly used in research in the field of COVID-19 regression modeling as well as present current literature based on Artificial Intelligence (AI) methods for regression tasks, like disease spread. Moreover, we discuss the applicability of Machine Learning (ML) and Evolutionary Computing (EC) methods that have focused on regressing epidemiology curves of COVID-19, and provide an overview of the usefulness of existing models in specific areas. An electronic literature search of the various databases was conducted to develop a comprehensive review of the latest AI-based approaches for modeling the spread of COVID-19. Finally, a conclusion is drawn from the observation of reviewed papers that AI-based algorithms have a clear application in COVID-19 epidemiological spread modeling and may be a crucial tool in the combat against coming pandemics.

show abstract

“…The majority of the time, medical pictures such as magnetic resonance imaging (MRI), positron emission tomography [5] (PET), and computed tomography [6] (CT) scans may identify lung nodules (CT). When it comes to recognizing [7] and detecting nodules, low-dose CT has emerged as the most sensitive and extensively utilized imaging technology. The great spatial resolution, non-invasive nature, and cheap cost of CT screening [8] are the three most important advantages of this procedure.…”

Section: Introductionmentioning

confidence: 99%

Lung Cancer Classification Using Squeeze and Excitation Convolutional Neural Networks with Grad Cam++ Class Activation Function

Joshua¹,

Bhattacharyya²,

Chakkravarthy³

et al. 2021

Self Cite

View full text Add to dashboard Cite

The leading cause of cancer-related death globally has been identified as lung cancer. Early lung nodule detection is critical for lung cancer therapy and patient survival. The Gard Cam++ Class Activation Function is used with a squeeze-and-excite network to provide a revolutionary method for differentiating malignant from benign lung nodules on CT scans. The new SENET (Squeeze-and-Excitation Networks) Grad Cam++ module, which combines the features calibration and discrimination benefits of SENET, has been shown to have a substantial potential for improving feature discriminability in lung cancer classification. According to the publicly available LUng Nodule Analysis 2016 (LUNA16) database, when assessed on 1230 nodules, the technique achieved an AUC of 0.9664 and an accuracy of 97.08% (600 malignant and 630 benign). The favorable results demonstrate the robustness of our technique to nodule classification, which we anticipate will be valuable in the future. The technology's objective is to aid radiologists in evaluating diagnostic data and differentiating benign from malignant lung nodules on CT images. To our knowledge, no systematic evaluation of SENET usefulness in classifying lung nodules has been done.

show abstract

Advanced Empirical Studies on Group Governance of the Novel Corona Virus, MERS, SARS and EBOLA: A Systematic Study

Cited by 17 publications

References 4 publications

Lung Cancer Segmentation with Three-Parameter Logistic Type Distribution燤odel

Lung Cancer Segmentation with Three-Parameter Logistic Type Distribution燤odel

Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review

Lung Cancer Classification Using Squeeze and Excitation Convolutional Neural Networks with Grad Cam++ Class Activation Function

Contact Info

Product

Resources

About