A Fusion Scheme of Texture Features for COVID-19 Detection of CT Scan Images

Zebari, Dilovan Asaad; Abdulazeez, Adnan Mohsin; Zeebaree, Diyar Qader; Salih, Merdin Shamal

doi:10.1109/icoase51841.2020.9436538

Cited by 19 publications

(7 citation statements)

References 20 publications

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“…Current ML-based methods that use AI evaluation measures to distinguish COVID-19 and normal patients outperformed the proposed model. In [39], the authors present a fusion scheme based on an ML system using three significant texture features, namely, local binary pattern (LBP), fractal dimension (FD), and grey level co-occurrence matrices (GLCM). In experimental results, to demonstrate the efficiency of the proposed scheme, we have collected 300 CT scan images from a publicly available database.…”

Section: Related Workmentioning

confidence: 99%

A Novel CovidDetNet Deep Learning Model for Effective COVID-19 Infection Detection Using Chest Radiograph Images

et al. 2022

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The suspected cases of COVID-19 must be detected quickly and accurately to avoid the transmission of COVID-19 on a large scale. Existing COVID-19 diagnostic tests are slow and take several hours to generate the required results. However, on the other hand, most X-rays or chest radiographs only take less than 15 min to complete. Therefore, we can utilize chest radiographs to create a solution for early and accurate COVID-19 detection and diagnosis to reduce COVID-19 patient treatment problems and save time. For this purpose, CovidDetNet is proposed, which comprises ten learnable layers that are nine convolutional layers and one fully-connected layer. The architecture uses two activation functions: the ReLu activation function and the Leaky Relu activation function and two normalization operations that are batch normalization and cross channel normalization, making it a novel COVID-19 detection model. It is a novel deep learning-based approach that automatically and reliably detects COVID-19 using chest radiograph images. Towards this, a fine-grained COVID-19 classification experiment is conducted to identify and classify chest radiograph images into normal, COVID-19 positive, and pneumonia. In addition, the performance of the proposed novel CovidDetNet deep learning model is evaluated on a standard COVID-19 Radiography Database. Moreover, we compared the performance of our approach with hybrid approaches in which we used deep learning models as feature extractors and support vector machines (SVM) as a classifier. Experimental results on the dataset showed the superiority of the proposed CovidDetNet model over the existing methods. The proposed CovidDetNet outperformed the baseline hybrid deep learning-based models by achieving a high accuracy of 98.40%.

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Section: Related Workmentioning

confidence: 99%

A Novel CovidDetNet Deep Learning Model for Effective COVID-19 Infection Detection Using Chest Radiograph Images

et al. 2022

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“…Preprints on medRxiv and bioRxiv regarding COVID‐19 yielded positive CT images, which show diverse kinds of COVID‐19. The CT images have various sizes because they were obtained from various sources (Zebari et al, 2020 ).…”

Section: Proposed Modelmentioning

confidence: 99%

“…In recent years, CT has used by many experts, owing to the possibility that an initial chest CT may reveal abnormal signs of COVID‐19 (Pan et al, 2020 ). Furthermore, CT has a quick turnaround time, high positive rate, and better diagnostic accuracy due to having access to pathology‐specific information (Xu et al, 2020 ; Zebari et al, 2020 ). In the literature, several works based on computerized tomography (CT) images were suggested as secondary examination for suspicious persons who hold COVID‐19 and shows symptoms even their RT‐PCR results were negative.…”

Section: Introductionmentioning

confidence: 99%

Effective hybrid deep learning model forCOVID‐19 patterns identification usingCTimages

Ibrahim¹,

Zebari

Mohammed

et al. 2022

Expert Systems

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Coronavirus disease 2019 (COVID‐19) has attracted significant attention of researchers from various disciplines since the end of 2019. Although the global epidemic situation is stabilizing due to vaccination, new COVID‐19 cases are constantly being discovered around the world. As a result, lung computed tomography (CT) examination, an aggregated identification technique, has been used to ameliorate diagnosis. It helps reveal missed diagnoses due to the ambiguity of nucleic acid polymerase chain reaction. Therefore, this study investigated how quickly and accurately hybrid deep learning (DL) methods can identify infected individuals with COVID‐19 on the basis of their lung CT images. In addition, this study proposed a developed system to create a reliable COVID‐19 prediction network using various layers starting with the segmentation of the lung CT scan image and ending with disease prediction. The initial step of the system starts with a proposed technique for lung segmentation that relies on a no‐threshold histogram‐based image segmentation method. Afterward, the GrabCut method was used as a post‐segmentation method to enhance segmentation outcomes and avoid over‐and under‐segmentation problems. Then, three pre‐trained models of standard DL methods, including Visual Geometry Group Network, convolutional deep belief network, and high‐resolution network, were utilized to extract the most affective features from the segmented images that can help to identify COVID‐19. These three described pre‐trained models were combined as a new mechanism to increase the system's overall prediction capabilities. A publicly available dataset, namely, COVID‐19 CT, was used to test the performance of the proposed model, which obtained a 95% accuracy rate. On the basis of comparison, the proposed model outperformed several state‐of‐the‐art studies. Because of its effectiveness in accurately screening COVID‐19 CT images, the developed model will potentially be valuable as an additional diagnostic tool for leading clinical professionals.

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“…The planned model is executed in MATLAB, and the successive score was calculated by comparing the key metrics with old models in terms of, accuracy, Precision, sensitivity, error rate, F-measure, and recall. CNN with Fuzzy (CNN-F) [21], Fusion schemes (FC) [22], bat optimization with Fuzzy (BO-F) technique [23], CNN with VGG16 [24], and Hidden Markov with U-net Architecture (HMUA) [25].…”

Section: Comparative Analysismentioning

confidence: 99%

“…State-of-the-art comparisonIt has taken more time to run the entire process Zebari et al[22] FSThe proposal helps the radiologist The determination accuracy depends on the quality of data in KNN Kaur et al[23] BO-F Health care data sets are used to attain correct predictionOverlapping occurs in the target classesHeidari et al [24] CNN and VGG16 Possible to use the large data set High correlation and high matrix dimensionality Marfak et al [25] HMUA Computation time low Inaccurate segmentation Proposed VbANF High accuracy and F1-score, High Precision, High sensitivity, and specificity, lower error rate -…”

mentioning

confidence: 99%

Vulture-Based AdaBoost-Feedforward Neural Frame Work for COVID-19 Prediction and Severity Analysis System

Mary¹,

Kumar

Venkatesan³

et al. 2022

Interdiscip Sci Comput Life Sci

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In today's scenario, many scientists and medical researchers have been involved in deep research for discovering the desired medicine to reduce the spread of COVID-19 disease. However, still, it is not the end. Hence, predicting the COVID possibility in an early stage is the most required matter to reduce the death risks. Therefore, many researchers have focused on designing an early prediction mechanism in the basis of deep learning (DL), machine learning (Ml), etc., on detecting the COVID virus and severity in the human body in an earlier stage. However, the complexity of X-ray images has made it difficult to attain the finest prediction accuracy. Hence, the present research work has aimed to develop a novel Vulture Based Adaboost-Feedforward Neural (VbAFN) scheme to forecast the COVID-19 severity early. Here, the chest X-ray images were employed to identify the COVID risk feature in humans. The preprocessing function is done in the initial phase; the error-free data is imported to the classification layer for the feature extraction and segmentation process. This investigation aims to track and segment the affected parts from the trained X-ray images by the vulture fitness and to segment them with a good exactness rate. Subsequently, the designed model has gained a better segmentation accuracy of 99.9% and a lower error rate of 0.0145, which is better than other compared models. Hence, this proposed model in medical applications will offer the finest results. Graphical abstract

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A Fusion Scheme of Texture Features for COVID-19 Detection of CT Scan Images

Cited by 19 publications

References 20 publications

A Novel CovidDetNet Deep Learning Model for Effective COVID-19 Infection Detection Using Chest Radiograph Images

A Novel CovidDetNet Deep Learning Model for Effective COVID-19 Infection Detection Using Chest Radiograph Images

Effective hybrid deep learning model forCOVID‐19 patterns identification usingCTimages

Vulture-Based AdaBoost-Feedforward Neural Frame Work for COVID-19 Prediction and Severity Analysis System

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