Detection of Oral Cavity Squamous Cell Carcinoma from Normal Epithelium of the Oral Cavity using Microscopic Images

Ukwuoma, Chiagoziem C.; Qin, Zhiguang; Heyat, Md Belal Bin; Khan, Haider Mohammed; Akhtar, Faijan; Masadeh, Mahmoud; Bamisile, Olusola; AlShorman, Omar; Nneji, Grace Ugochi

doi:10.1109/dasa54658.2022.9765023

Cited by 6 publications

(4 citation statements)

References 45 publications

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“…The robustness of the suggested model was assessed using a variety of evaluation indicators. Accuracy, precision, specificity, F1_score, sensitivity, and area under a receiver operating characteristic curve are among the measurements [ 62 , 63 , 64 , 65 , 66 , 67 , 68 ]. TP stands for True Positive, FP for False Positive, TN for True Negative, and FN for False Negative.…”

Section: Methodsmentioning

confidence: 99%

Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images

et al. 2022

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According to research, classifiers and detectors are less accurate when images are blurry, have low contrast, or have other flaws which raise questions about the machine learning model’s ability to recognize items effectively. The chest X-ray image has proven to be the preferred image modality for medical imaging as it contains more information about a patient. Its interpretation is quite difficult, nevertheless. The goal of this research is to construct a reliable deep-learning model capable of producing high classification accuracy on chest x-ray images for lung diseases. To enable a thorough study of the chest X-ray image, the suggested framework first derived richer features using an ensemble technique, then a global second-order pooling is applied to further derive higher global features of the images. Furthermore, the images are then separated into patches and position embedding before analyzing the patches individually via a vision transformer approach. The proposed model yielded 96.01% sensitivity, 96.20% precision, and 98.00% accuracy for the COVID-19 Radiography Dataset while achieving 97.84% accuracy, 96.76% sensitivity and 96.80% precision, for the Covid-ChestX-ray-15k dataset. The experimental findings reveal that the presented models outperform traditional deep learning models and other state-of-the-art approaches provided in the literature.

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Section: Methodsmentioning

confidence: 99%

Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images

et al. 2022

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“…Adaboost classifier, an ensemble learning method, also called meta-learning, is an iterative approach that incorporates learning from weak classifiers and turning them into strong ones [ 49 , 50 , 51 ]. In each iteration, the data samples are trained based on various weights of the classifier to minimize the training errors that can assure accurate predictions.…”

Section: Methodsmentioning

confidence: 99%

A Novel Smart Belt for Anxiety Detection, Classification, and Reduction Using IIoMT on Students’ Cardiac Signal and MSY

et al. 2022

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The prevalence of anxiety among university students is increasing, resulting in the negative impact on their academic and social (behavioral and emotional) development. In order for students to have competitive academic performance, the cognitive function should be strengthened by detecting and handling anxiety. Over a period of 6 weeks, this study examined how to detect anxiety and how Mano Shakti Yoga (MSY) helps reduce anxiety. Relying on cardiac signals, this study follows an integrated detection-estimation-reduction framework for anxiety using the Intelligent Internet of Medical Things (IIoMT) and MSY. IIoMT is the integration of Internet of Medical Things (wearable smart belt) and machine learning algorithms (Decision Tree (DT), Random Forest (RF), and AdaBoost (AB)). Sixty-six eligible students were selected as experiencing anxiety detected based on the results of self-rating anxiety scale (SAS) questionnaire and a smart belt. Then, the students were divided randomly into two groups: experimental and control. The experimental group followed an MSY intervention for one hour twice a week, while the control group followed their own daily routine. Machine learning algorithms are used to analyze the data obtained from the smart belt. MSY is an alternative improvement for the immune system that helps reduce anxiety. All the results illustrate that the experimental group reduced anxiety with a significant (p < 0.05) difference in group × time interaction compared to the control group. The intelligent techniques achieved maximum accuracy of 80% on using RF algorithm. Thus, students can practice MSY and concentrate on their objectives by improving their intelligence, attention, and memory.

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“…SC is computed based on AoD and SoE. The other indicators such as recall, precision, specificity, F1-score, and accuracy [ 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 ] are computed in Equations (1)–(5) below:

where TP, TN, FN, and FP represent true positive, true negative, false negative, and false positive.…”

Section: Performance Indicatormentioning

confidence: 99%

Yoga Meets Intelligent Internet of Things: Recent Challenges and Future Directions

et al. 2023

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The physical and mental health of people can be enhanced through yoga, an excellent form of exercise. As part of the breathing procedure, yoga involves stretching the body organs. The guidance and monitoring of yoga are crucial to ripe the full benefits of it, as wrong postures possess multiple antagonistic effects, including physical hazards and stroke. The detection and monitoring of the yoga postures are possible with the Intelligent Internet of Things (IIoT), which is the integration of intelligent approaches (machine learning) and the Internet of Things (IoT). Considering the increment in yoga practitioners in recent years, the integration of IIoT and yoga has led to the successful implementation of IIoT-based yoga training systems. This paper provides a comprehensive survey on integrating yoga with IIoT. The paper also discusses the multiple types of yoga and the procedure for the detection of yoga using IIoT. Additionally, this paper highlights various applications of yoga, safety measures, various challenges, and future directions. This survey provides the latest developments and findings on yoga and its integration with IIoT.

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Detection of Oral Cavity Squamous Cell Carcinoma from Normal Epithelium of the Oral Cavity using Microscopic Images

Cited by 6 publications

References 45 publications

Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images

Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images

A Novel Smart Belt for Anxiety Detection, Classification, and Reduction Using IIoMT on Students’ Cardiac Signal and MSY

Yoga Meets Intelligent Internet of Things: Recent Challenges and Future Directions

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