COV-ADSX: An Automated Detection System using X-ray Images, Deep Learning, and XGBoost for COVID-19

Dadfar

et al. 2022

Sci Rep

Self Cite

Cement production is one of the most energy-intensive manufacturing industries, and the milling circuit of cement plants consumes around 4% of a year's global electrical energy production. It is well understood that modeling and digitalizing industrial-scale processes would help control production circuits better, improve efficiency, enhance personal training systems, and decrease plants' energy consumption. This tactical approach could be integrated using conscious lab (CL) as an innovative concept in the internet age. Surprisingly, no CL has been reported for the milling circuit of a cement plant. A robust CL interconnect datasets originated from monitoring operational variables in the plants and translating them to human basis information using explainable artificial intelligence (EAI) models. By initiating a CL for an industrial cement vertical roller mill (VRM), this study conducted a novel strategy to explore relationships between VRM monitored operational variables and their representative energy consumption factors (output temperature and motor power). Using SHapley Additive exPlanations (SHAP) as one of the most recent EAI models accurately helped fill the lack of information about correlations within VRM variables. SHAP analyses highlighted that working pressure and input gas rate with positive relationships are the key factors influencing energy consumption. eXtreme Gradient Boosting (XGBoost) as a powerful predictive tool could accurately model energy representative factors by R-square ever 0.80 in the testing phase. Comparison assessments indicated that SHAP-XGBoost could provide higher accuracy for VRM-CL structure than conventional modeling tools (Pearson correlation, Random Forest, and Support vector regression.

Section: Methodsmentioning

confidence: 99%

Modeling of energy consumption factors for an industrial cement vertical roller mill by SHAP-XGBoost: a "conscious lab" approach

Fatahi

Dadfar

et al. 2022

Sci Rep

Self Cite

“…Specificity: Mathematically, it is the result of dividing true negative cases by the sum of true negative and false positive cases as shown in ( 5): TN Specificity TN FP (5) Precision: Mathematically, it is the result of dividing true positive cases by the sum of true positive and false positive cases as shown in (6).…”

Section: Tp Sensitivity Tp Fn (4)mentioning

confidence: 99%

“…The disease was caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and called COVID-19 virus by the World Health Organization [3]. The symptoms of this disease, which might be mild or severe, included fever, dry cough, sore throat, headache, fatigue, and shortness of breath [4], [5]. The best ways to prevent the spread of this virus have been to observe a proper physical distance at all times, avoid touching the eyes, nose, and mouth with unwashed hands, and use a mask to completely cover the nose and mouth [6].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBM

Ezzoddin

2022 International Conference on Machine Vision and Image Processing (MVIP)

Dorrigiv

2022

Self Cite

The Coronavirus was detected in Wuhan, China in late 2019 and then led to a pandemic with a rapid worldwide outbreak. The number of infected people has been swiftly increasing since then. Therefore, in this study, an attempt was made to propose a new and efficient method for automatic diagnosis of Corona disease from X-ray images using Deep Neural Networks (DNNs). In the proposed method, the DensNet169 was used to extract the features of the patients' Chest X-Ray (CXR) images. The extracted features were given to a feature selection algorithm (i.e., ANOVA) to select a number of them. Finally, the selected features were classified by LightGBM algorithm. The proposed approach was evaluated on the ChestX-ray8 dataset and reached 99.20% and 94.22% accuracies in the two-class (i.e., COVID-19 and No-findings) and multi-class (i.e., COVID-19, Pneumonia, and No-findings) classification problems, respectively.

“…They obtained an average accuracy of 98.24% for the two classes and 98.70% for the three classes. In 2022, Hasani and Nasiri [13] proposed COV-ADSX, an automated COVID-19 detection system, which used the proposed algorithm in [12]. They used the Django web framework for the implementation of the COV-ADSX.…”

Section: Introductionmentioning

confidence: 99%

Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM

2022 IEEE World AI IoT Congress (AIIoT)

Kheyroddin

Dorrigiv

et al. 2022

Self Cite

The COVID-19 disease was first discovered in Wuhan, China, and spread quickly worldwide. After the COVID-19 pandemic, many researchers have begun to identify a way to diagnose the COVID-19 using chest X-ray images. The early diagnosis of this disease can significantly impact the treatment process. In this article, we propose a new technique that is faster and more accurate than the other methods reported in the literature. The proposed method uses a combination of DenseNet169 and MobileNet Deep Neural Networks to extract the features of the patient's X-ray images. Using the univariate feature selection algorithm, we refined the features for the most important ones. Then we applied the selected features as input to the LightGBM (Light Gradient Boosting Machine) algorithm for classification. To assess the effectiveness of the proposed method, the ChestX-ray8 dataset, which includes 1125 X-ray images of the patient's chest, was used. The proposed method achieved 98.54% and 91.11% accuracies in the two-class (COVID-19, Healthy) and multi-class (COVID-19, Healthy, Pneumonia) classification problems, respectively. It is worth mentioning that we have used Gradient-weighted Class Activation Mapping (Grad-CAM) for further analysis.