Noninvasive Detection of Potentially Precancerous Lesions of Vocal Fold Based on Glottal Wave Signal and SVM Approaches

Aicha, Anis Ben

doi:10.1016/j.procs.2018.07.293

Cited by 15 publications

(5 citation statements)

References 21 publications

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“…The original data were processed by using boxplot. 31,32) The boxplot consists of five statistics, including lower quartile (Q 1 ), median (Q 2 ), upper quartile (Q 3 ), Q 1 -1.5IQR, and Q 3 + 1.5IQR. The interquartile range (IQR) is defined as…”

Section: Data Cleansingmentioning

confidence: 99%

A Hybrid Modeling Method Based on Expert Control and Deep Neural Network for Temperature Prediction of Molten Steel in LF

et al. 2022

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The temperature control of molten steel in ladle furnace (LF) has a critical impact on steelmaking production. In this work, production data were collected from a steelmaking plant and a hybrid model based on expert control and deep neural network (DNN) was established to predict the molten steel temperature in LF. In order to obtain the optimal DNN model, the trial and error method was used to determine the hyperparameters. And the optimal architecture of DNN model corresponds to the hidden layers of 4, hidden layer neurons of 35, iterations of 3 000, and learning rate of 0.2. Compared with the multiple linear regression model and the shallow neural network model, the DNN model exhibits stronger generalisation performance and higher accuracy. The coefficient of determination (R 2 ), correlation coefficient (r), mean square error (MSE), and root-mean-square error (RMSE) of the optimal DNN model reached 0.897, 0.947, 2.924, 1.710, respectively. Meanwhile, in the error scope of temperature from − 5 to 5°C, the hit ratio of the hybrid model acquired 99.4%. The results demonstrate that the proposed model is effective to predict temperature of molten steel in LF.

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Section: Data Cleansingmentioning

confidence: 99%

A Hybrid Modeling Method Based on Expert Control and Deep Neural Network for Temperature Prediction of Molten Steel in LF

et al. 2022

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“…Therefore, outliers were removed to ensure the reliability of machine learning model. A boxplot has proven to be a very effective tool for outlier detection [16][17][18]. The structure of a boxplot is shown in Figure 3.…”

Section: Outlier Detectionmentioning

confidence: 99%

Prediction of Central Carbon Segregation in Continuous Casting Billet Using A Regularized Extreme Learning Machine Model

Zou

Zhang

Liu

et al. 2019

Metals

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Central carbon segregation is a typical internal defect of continuous cast steel billets. Real-time and accurate carbon segregation prediction is of great significance for lean control of the production quality in continuous casting processes. In this paper, a data-driven regularized extreme learning machine (R-ELM) model is proposed for the prediction of carbon segregation index (CSI). To improve model performance, outliers in industrial data were eliminated by means of boxplot tool. Besides, Pearson correlation combined with grey relational analysis (GRA) was conducted to avoid multicollinearity and redundancy in input variables. The new model shows potential to evaluate online quality of steel billets. When predictive errors were within ±0.03 and ±0.025, the prediction accuracy of the R-ELM model was 94% and 89%, respectively, which was higher than that of the multiple linear regression (MLR) model and ELM model. Moreover, the effects of several key continuous casting process parameters on CSI were investigated based on the predictions of the R-ELM model via response surface analysis. The conclusions are consistent with the metallurgical mechanism, and the predictive values of the R-ELM model agree well with experimental values, which further verifies the correctness and generalization ability of the R-ELM model.

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“…In this context, multiple computer-rooted schemes have been utilized over larynx endoscopic pictures. Such approaches may aid otolaryngologists by offering complementary datasets about the cancer stage along with the vascular tree's main characteristics and the larynx portion of the entire epithelial tissue [12,13]. Figure 1 illustrates a few of the most common current tests preferred by clinicians for laryngeal cancer identification.…”

Section: Introductionmentioning

confidence: 99%

An Improvised Deep-Learning-Based Mask R-CNN Model for Laryngeal Cancer Detection Using CT Images

Sahoo

Mishra

Panigrahi

et al. 2022

Sensors

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Recently, laryngeal cancer cases have increased drastically across the globe. Accurate treatment for laryngeal cancer is intricate, especially in the later stages. This type of cancer is an intricate malignancy inside the head and neck area of patients. In recent years, diverse diagnosis approaches and tools have been developed by researchers for helping clinical experts to identify laryngeal cancer effectively. However, these existing tools and approaches have diverse issues related to performance constraints such as lower accuracy in the identification of laryngeal cancer in the initial stage, more computational complexity, and large time consumption in patient screening. In this paper, the authors present a novel and enhanced deep-learning-based Mask R-CNN model for the identification of laryngeal cancer and its related symptoms by utilizing diverse image datasets and CT images in real time. Furthermore, our suggested model is capable of capturing and detecting minor malignancies of the larynx portion in a significant and faster manner in the real-time screening of patients, and it saves time for the clinicians, allowing for more patient screening every day. The outcome of the suggested model is enhanced and pragmatic and obtained an accuracy of 98.99%, precision of 98.99%, F1 score of 97.99%, and recall of 96.79% on the ImageNet dataset. Several studies have been performed in recent years on laryngeal cancer detection by using diverse approaches from researchers. For the future, there are vigorous opportunities for further research to investigate new approaches for laryngeal cancer detection by utilizing diverse and large dataset images.

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Noninvasive Detection of Potentially Precancerous Lesions of Vocal Fold Based on Glottal Wave Signal and SVM Approaches

Cited by 15 publications

References 21 publications

A Hybrid Modeling Method Based on Expert Control and Deep Neural Network for Temperature Prediction of Molten Steel in LF

A Hybrid Modeling Method Based on Expert Control and Deep Neural Network for Temperature Prediction of Molten Steel in LF

Prediction of Central Carbon Segregation in Continuous Casting Billet Using A Regularized Extreme Learning Machine Model

An Improvised Deep-Learning-Based Mask R-CNN Model for Laryngeal Cancer Detection Using CT Images

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