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Mohan R, Rama A, Raja RK, Shaik MR, Khan M, Shaik B, Rajinikanth V. OralNet: Fused Optimal Deep Features Framework for Oral Squamous Cell Carcinoma Detection. Biomolecules 2023; 13:1090. [PMID: 37509126 PMCID: PMC10377094 DOI: 10.3390/biom13071090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/17/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023] Open
Abstract
Humankind is witnessing a gradual increase in cancer incidence, emphasizing the importance of early diagnosis and treatment, and follow-up clinical protocols. Oral or mouth cancer, categorized under head and neck cancers, requires effective screening for timely detection. This study proposes a framework, OralNet, for oral cancer detection using histopathology images. The research encompasses four stages: (i) Image collection and preprocessing, gathering and preparing histopathology images for analysis; (ii) feature extraction using deep and handcrafted scheme, extracting relevant features from images using deep learning techniques and traditional methods; (iii) feature reduction artificial hummingbird algorithm (AHA) and concatenation: Reducing feature dimensionality using AHA and concatenating them serially and (iv) binary classification and performance validation with three-fold cross-validation: Classifying images as healthy or oral squamous cell carcinoma and evaluating the framework's performance using three-fold cross-validation. The current study examined whole slide biopsy images at 100× and 400× magnifications. To establish OralNet's validity, 3000 cropped and resized images were reviewed, comprising 1500 healthy and 1500 oral squamous cell carcinoma images. Experimental results using OralNet achieved an oral cancer detection accuracy exceeding 99.5%. These findings confirm the clinical significance of the proposed technique in detecting oral cancer presence in histology slides.
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Affiliation(s)
- Ramya Mohan
- Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India
| | - Arunmozhi Rama
- Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India
| | - Ramalingam Karthik Raja
- Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India
| | - Mohammed Rafi Shaik
- Department of Chemistry, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
| | - Mujeeb Khan
- Department of Chemistry, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
| | - Baji Shaik
- School of Chemical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
| | - Venkatesan Rajinikanth
- Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India
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Pal R, Adhikari D, Heyat MBB, Ullah I, You Z. Yoga Meets Intelligent Internet of Things: Recent Challenges and Future Directions. Bioengineering (Basel) 2023; 10:459. [PMID: 37106646 PMCID: PMC10135646 DOI: 10.3390/bioengineering10040459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/30/2023] [Accepted: 04/06/2023] [Indexed: 04/29/2023] Open
Abstract
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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Affiliation(s)
- Rishi Pal
- Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Deepak Adhikari
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | - Inam Ullah
- Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam 13120, Republic of Korea
| | - Zili You
- Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
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Alphonse AS, Benifa JVB, Muaad AY, Chola C, Heyat MBB, Murshed BAH, Abdel Samee N, Alabdulhafith M, Al-antari MA. A Hybrid Stacked Restricted Boltzmann Machine with Sobel Directional Patterns for Melanoma Prediction in Colored Skin Images. Diagnostics (Basel) 2023; 13:diagnostics13061104. [PMID: 36980412 PMCID: PMC10047753 DOI: 10.3390/diagnostics13061104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/07/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023] Open
Abstract
Melanoma, a kind of skin cancer that is very risky, is distinguished by uncontrolled cell multiplication. Melanoma detection is of the utmost significance in clinical practice because of the atypical border structure and the numerous types of tissue it can involve. The identification of melanoma is still a challenging process for color images, despite the fact that numerous approaches have been proposed in the research that has been done. In this research, we present a comprehensive system for the efficient and precise classification of skin lesions. The framework includes preprocessing, segmentation, feature extraction, and classification modules. Preprocessing with DullRazor eliminates skin-imaging hair artifacts. Next, Fully Connected Neural Network (FCNN) semantic segmentation extracts precise and obvious Regions of Interest (ROIs). We then extract relevant skin image features from ROIs using an enhanced Sobel Directional Pattern (SDP). For skin image analysis, Sobel Directional Pattern outperforms ABCD. Finally, a stacked Restricted Boltzmann Machine (RBM) classifies skin ROIs. Stacked RBMs accurately classify skin melanoma. The experiments have been conducted on five datasets: Pedro Hispano Hospital (PH2), International Skin Imaging Collaboration (ISIC 2016), ISIC 2017, Dermnet, and DermIS, and achieved an accuracy of 99.8%, 96.5%, 95.5%, 87.9%, and 97.6%, respectively. The results show that a stack of Restricted Boltzmann Machines is superior for categorizing skin cancer types using the proposed innovative SDP.
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Affiliation(s)
- A. Sherly Alphonse
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India
| | - J. V. Bibal Benifa
- Department of Studies in Computer Science and Engineering, Indian Institute of Information Technology, Kottayam 686635, India
- Correspondence: (J.V.B.B.); (M.A.); (M.A.A.-a.)
| | - Abdullah Y. Muaad
- Department of Studies in Computer Science, University of Mysore, Manasagangothri, Mysore 570006, India
| | - Channabasava Chola
- Department of Studies in Computer Science and Engineering, Indian Institute of Information Technology, Kottayam 686635, India
| | - Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | | | - Nagwan Abdel Samee
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Maali Alabdulhafith
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
- Correspondence: (J.V.B.B.); (M.A.); (M.A.A.-a.)
| | - Mugahed A. Al-antari
- Department of Artificial Intelligence, College of Software and Convergence Technology, Daeyang AI Center, Sejong University, Seoul 05006, Republic of Korea
- Correspondence: (J.V.B.B.); (M.A.); (M.A.A.-a.)
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Pal R, Adhikari D, Heyat MBB, Guragai B, Lipari V, Brito Ballester J, De la Torre Díez I, Abbas Z, Lai D. A Novel Smart Belt for Anxiety Detection, Classification, and Reduction Using IIoMT on Students' Cardiac Signal and MSY. Bioengineering (Basel) 2022; 9:bioengineering9120793. [PMID: 36550999 PMCID: PMC9774730 DOI: 10.3390/bioengineering9120793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/07/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022] Open
Abstract
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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Affiliation(s)
- Rishi Pal
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Deepak Adhikari
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
- Centre for VLSI and Embedded System Technologies, International Institute of Information Technology, Hyderabad 500032, India
- Department of Science and Engineering, Novel Global Community Educational Foundation, Hebersham, NSW 2770, Australia
- Correspondence: (M.B.B.H.); (D.L.)
| | - Bishal Guragai
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Vivian Lipari
- Research Group on Foods, Nutritional Biochemistry and Health Universidad Europea Del Atlántico, Isabel Torres, 39011 Santander, Spain
- Research Group on Foods, Nutritional Biochemistry and Health Universidade Internacional do Cuanza, Cuito EN250, Angola
- Research Group on Foods, Nutritional Biochemistry and Health Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA
| | - Julien Brito Ballester
- Research Group on Foods, Nutritional Biochemistry and Health Universidad Europea Del Atlántico, Isabel Torres, 39011 Santander, Spain
- Research Group on Foods, Nutritional Biochemistry and Health Universidade Internacional do Cuanza, Cuito EN250, Angola
- Research Group on Foods, Nutritional Biochemistry and Health Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA
| | - Isabel De la Torre Díez
- Department of Signal Theory and Communications and Telematic Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
| | - Zia Abbas
- Centre for VLSI and Embedded System Technologies, International Institute of Information Technology, Hyderabad 500032, India
| | - Dakun Lai
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
- Correspondence: (M.B.B.H.); (D.L.)
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