Segmentation and Evaluation of COVID-19 Lesion from CT scan Slices - A Study with Kapur/Otsu Function and Cuckoo Search Algorithm

Satapathy, Suresh Chandra; Hemanth, D. Jude; Kadry, Seifedine; Manogaran, Gunasekaran; Hannon, Naeem M. S.; Rajinikanth, Venkatesan

doi:10.21203/rs.3.rs-40148/v1

Cited by 16 publications

(8 citation statements)

References 33 publications

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“…At the beginning of 2020, the sudden outbreak of COVID-19 (Loey et al, 2020;Satapathy et al, 2020) has brought a profound impact on the production and life of the people across the country as well as the economic and social development. However, it has also tested the effectiveness of a country's ecological governance.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Research on the impact of managers’ green environmental awareness and strategic intelligence on corporate green product innovation strategic performance

Yang

Bei-lei

2021

Ann Oper Res

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This article discusses the impact of corporate managers' green environmental awareness and strategic intelligence on the formulation of corporate green product innovation strategies and their relationship with marketing performance. As the pre-factors affecting innovation, the green environmental awareness and strategic intelligence will affect the formulation and implementation of corporate green product innovation strategies, thereby promoting corporate performance. According to the regression analysis of 367 questionnaires from heavily polluting enterprises, it can be seen that managers’ green environmental awareness is positively correlated with green product technological innovation and design innovation strategies. Managers’ meta-cognitive strategic intelligence does not have a moderating effect on the relationship between green environmental awareness and green product technological innovation strategy, but has a significant positive moderating effect on the green product design innovation strategy. Managers’ motivational strategic intelligence has a positive influence on the relationship between green environmental awareness and green product technology and design innovation strategy. In addition, the implementation of green product innovation strategies directly promotes the improvement of marketing performance. The above conclusions can provide inspiration or companies during the new opportunity period to transform managers' environmental awareness and strategic intelligence into corporate performance by using green product innovation strategies.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Research on the impact of managers’ green environmental awareness and strategic intelligence on corporate green product innovation strategic performance

Yang

Bei-lei

2021

Ann Oper Res

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“…Hence, ROI can replace the original image in image processing, which can reduce the amount of calculations and deal better with the rapid growth of image data [41]. Segmentation of ROI from the enhanced image is widely employed by researchers with automated and semi-automated techniques [42]. In this work, the lung structure in the CT images was automatically segmented using Python's OpenCV and scikit-image libraries.…”

Section: Methodsmentioning

confidence: 99%

Fusion of Moment Invariant Method and Deep Learning Algorithm for COVID-19 Classification

Moung

Hou

Sufian

et al. 2021

BDCC

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The COVID-19 pandemic has resulted in a global health crisis. The rapid spread of the virus has led to the infection of a significant population and millions of deaths worldwide. Therefore, the world is in urgent need of a fast and accurate COVID-19 screening. Numerous researchers have performed exceptionally well to design pioneering deep learning (DL) models for the automatic screening of COVID-19 based on computerised tomography (CT) scans; however, there is still a concern regarding the performance stability affected by tiny perturbations and structural changes in CT images. This paper proposes a fusion of a moment invariant (MI) method and a DL algorithm for feature extraction to address the instabilities in the existing COVID-19 classification models. The proposed method incorporates the MI-based features into the DL models using the cascade fusion method. It was found that the fusion of MI features with DL features has the potential to improve the sensitivity and accuracy of the COVID-19 classification. Based on the evaluation using the SARS-CoV-2 dataset, the fusion of VGG16 and Hu moments shows the best result with 90% sensitivity and 93% accuracy.

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“… 19 Hence, segmentation is the main issue impacting the outcome of both qualitative and quantitative studies. 12 , 19 , 20 Although several segmentation approaches including manual delineation, semiautomated 21 and fully automated 20 techniques have been applied to CT images for COVID‐19 management, they are still facing serious challenges to produce robust and dependable outcomes.…”

Section: Introductionmentioning

confidence: 99%

COLI‐Net: Deep learning‐assisted fully automated COVID‐19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images

Shiri

Arabi

Salimi

et al. 2021

Int J Imaging Syst Tech

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We present a deep learning (DL)‐based automated whole lung and COVID‐19 pneumonia infectious lesions (COLI‐Net) detection and segmentation from chest computed tomography (CT) images. This multicenter/multiscanner study involved 2368 (347′259 2D slices) and 190 (17 341 2D slices) volumetric CT exams along with their corresponding manual segmentation of lungs and lesions, respectively. All images were cropped, resized, and the intensity values clipped and normalized. A residual network with non‐square Dice loss function built upon TensorFlow was employed. The accuracy of lung and COVID‐19 lesions segmentation was evaluated on an external reverse transcription‐polymerase chain reaction positive COVID‐19 dataset (7′333 2D slices) collected at five different centers. To evaluate the segmentation performance, we calculated different quantitative metrics, including radiomic features. The mean Dice coefficients were 0.98 ± 0.011 (95% CI, 0.98–0.99) and 0.91 ± 0.038 (95% CI, 0.90–0.91) for lung and lesions segmentation, respectively. The mean relative Hounsfield unit differences were 0.03 ± 0.84% (95% CI, −0.12 to 0.18) and −0.18 ± 3.4% (95% CI, −0.8 to 0.44) for the lung and lesions, respectively. The relative volume difference for lung and lesions were 0.38 ± 1.2% (95% CI, 0.16–0.59) and 0.81 ± 6.6% (95% CI, −0.39 to 2), respectively. Most radiomic features had a mean relative error less than 5% with the highest mean relative error achieved for the lung for the range first‐order feature (−6.95%) and least axis length shape feature (8.68%) for lesions. We developed an automated DL‐guided three‐dimensional whole lung and infected regions segmentation in COVID‐19 patients to provide fast, consistent, robust, and human error immune framework for lung and pneumonia lesion detection and quantification.

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Segmentation and Evaluation of COVID-19 Lesion from CT scan Slices - A Study with Kapur/Otsu Function and Cuckoo Search Algorithm

Cited by 16 publications

References 33 publications

RETRACTED ARTICLE: Research on the impact of managers’ green environmental awareness and strategic intelligence on corporate green product innovation strategic performance

RETRACTED ARTICLE: Research on the impact of managers’ green environmental awareness and strategic intelligence on corporate green product innovation strategic performance

Fusion of Moment Invariant Method and Deep Learning Algorithm for COVID-19 Classification

COLI‐Net: Deep learning‐assisted fully automated COVID‐19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images

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