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Submit a ProposalHuman Cancer Diagnosis and Detection Using Exascale Computing
 | Edited by Kapil Joshi and Somil Kumar Gupta
Copyright: 2024 | Status: Published ISBN: 9781394197675 | Hardcover | 328 pages Price: $195 USD  |
One Line Description
The book provides an in-depth exploration of how high-performance
computing, particularly exascale computing, can be used to revolutionize
cancer diagnosis and detection; it also serves as a bridge between the worlds of
computational science and clinical oncology.
Audience
This book has a wide audience from both computer sciences (information technology, computer vision, artificial intelligence, software engineering, applied mathematics) and the medical field (biomedical engineering, bioinformatics, oncology). Researchers, practitioners and students will find this groundbreaking book novel and very useful.
This book has a wide audience from both computer sciences (information technology, computer vision, artificial intelligence, software engineering, applied mathematics) and the medical field (biomedical engineering, bioinformatics, oncology). Researchers, practitioners and students will find this groundbreaking book novel and very useful.
Description
Exascale computing has the potential to increase our ability in terms of computation to develop efficient methods for a better healthcare system. This technology promises to revolutionize cancer diagnosis and detection, ushering in an era of unprecedented precision, speed, and efficiency. The fusion of exascale computing with the field of oncology has the potential to redefine the boundaries of what is possible in the fight against cancer.
The book is a comprehensive exploration of this transformative unification of science, medicine, and technology. It delves deeply into the realm of exascale computing and its profound implications for cancer research and patient care. The 18 chapters are authored by experts from diverse fields who have dedicated their careers to pushing the boundaries of what is achievable in the realm of cancer diagnosis and detection.
The chapters cover a wide range of topics, from the fundamentals of exascale
computing and its application to cancer genomics to the development of advanced
imaging techniques and machine learning algorithms. Explored is the integration
of data analytics, artificial intelligence, and high-performance computing to move
cancer research to the next phase and support the creation of novel medical tools and technology for the detection and diagnosis of cancer.
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Exascale computing has the potential to increase our ability in terms of computation to develop efficient methods for a better healthcare system. This technology promises to revolutionize cancer diagnosis and detection, ushering in an era of unprecedented precision, speed, and efficiency. The fusion of exascale computing with the field of oncology has the potential to redefine the boundaries of what is possible in the fight against cancer.
The book is a comprehensive exploration of this transformative unification of science, medicine, and technology. It delves deeply into the realm of exascale computing and its profound implications for cancer research and patient care. The 18 chapters are authored by experts from diverse fields who have dedicated their careers to pushing the boundaries of what is achievable in the realm of cancer diagnosis and detection.
The chapters cover a wide range of topics, from the fundamentals of exascale
computing and its application to cancer genomics to the development of advanced
imaging techniques and machine learning algorithms. Explored is the integration
of data analytics, artificial intelligence, and high-performance computing to move
cancer research to the next phase and support the creation of novel medical tools and technology for the detection and diagnosis of cancer.
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Author / Editor Details
Kapil Joshi, PhD, is an assistant professor in the Computer Science & Engineering Department, Uttaranchal Institute of Technology in Dehradun, India. His doctorate was on image quality enhancement using fusion techniques. He has 8 years of academic experience and has published patents, research papers, and two books. In 2021, he was awarded the ‘Best Young Researcher’ Award in Global Education and Corporate Leadership received by Life Way Tech India Pvt. Ltd.
Kapil Joshi, PhD, is an assistant professor in the Computer Science & Engineering Department, Uttaranchal Institute of Technology in Dehradun, India. His doctorate was on image quality enhancement using fusion techniques. He has 8 years of academic experience and has published patents, research papers, and two books. In 2021, he was awarded the ‘Best Young Researcher’ Award in Global Education and Corporate Leadership received by Life Way Tech India Pvt. Ltd.
Somil Kumar Gupta is an assistant professor in the School of Computing, DIT University, Dehradun, India. He has fourteen years of experience in academics
and research and has published many research articles in reputed international and national journals and conferences as well as more than 16 patents with the Indian Patent Office.
and research and has published many research articles in reputed international and national journals and conferences as well as more than 16 patents with the Indian Patent Office.
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Table of Contents
Preface
1. Evaluating the Impact of Healthcare 4.0 on the Performance of Hospitals
Pramod Kumar, Nitu Maurya, Keerthiraj, Somanchi Hari Krishna, Geetha Manoharan
and Anupama Bharti
1.1 Introduction
1.2 Literature Review
1.3 Methodology
1.3.1 Selection of the Sample and Characterization
1.3.2 Creation of a Data-Gathering Tool and Measures
1.3.3 Inspection of the Conceptions’ Reliability and Validity
1.3.4 Data Evaluation
1.4 Result and Discussion
1.5 Conclusion
References
2. Human Breast Cancer Classification Employing the Machine Learning Ensemble
Sreenivas Mekala, S. Srinivasulu Raju, M. Gomathi, Naga Venkateshwara Rao K., Kothandaraman D. and Saurabh Sharma
2.1 Introduction
2.1.1 Breast Cancer Symptoms and Signs
2.1.2 Breast Cancer Risk Factors
2.1.3 Disease Prediction Using Machine Learning
2.2 Literature Review
2.3 Methodology
2.3.1 Bayesian Network
2.3.2 Radial Basis Function
2.3.3 Ensemble Learning
2.3.4 The Suggested Algorithm
2.4 Results and Discussion
2.5 Conclusion
References
3. Multi-Objective Differential Development Using DNN for Multimodality Medical Image Fusion
M. Ranjith Kumar, Abhishek Dondapati, Dilip Kumar Sharma, Prakash Pareek, Rajchandar K. and S. Shalini
3.1 Introduction
3.2 Literature Review
3.3 Methodology
3.3.1 Non-Subsampled Contourlet Transform
3.3.2 Deep Xception Mode Feature Extraction
3.3.3 Differential Evolutions with Several Objectives for Feature Selection
3.3.4 Fusion of High-Frequency Bands
3.4 Result and Discussion
3.4.1 Visual Evaluation
3.4.2 Quantitative Research
3.5 Conclusion
References
4. Multimodal Deep Learning Analysis for Biomedical Data Fusion
Divyanshu Sinha, B. Jogeswara Rao, D. Khalandar Basha, Parvathapuram Pavan Kumar, N. Shilpa and Saurabh Sharma
4.1 Introduction
4.2 Literature Review
4.3 Methodology
4.3.1 Early Fusion
4.3.2 Intermediate Fusion
4.3.3 Late Fusion
4.4 Results and Discussion
4.5 Conclusion
References
5. Developing Robot-Based Neurorehabilitation Exercises Using a Teaching–Training Process
W. Vinu, Sonali Vyas, A. Chandrashekhar, T. Ch. Anil Kumar, T. Raghu and Mohit Tiwari
5.1 Introduction
5.1.1 Research Gap
5.1.2 Research Aim
5.2 Literature Review
5.3 Research Methodology
5.4 Results
5.5 Conclusion
5.6 Future Research Directions
References
6. Investigation on Introduction to Heterogeneous Exascale Computing in the Medical Field
M. Pyingkodi, Raju Shanmugam, Dilip Kumar Sharma, Deepesh Lall, S. Deepan and B. Dasu
6.1 Introduction
6.1.1 Research Gap
6.2 Literature Review
6.3 Research Methodology
6.4 Results and Discussion
6.5 Conclusion
6.6 Future Research Direction
References
7. Adoption of Cloud Computing in the Healthcare Field Using the SEM Approach
R. Chithambaramani, C. Balakumar, Dilip Kumar Sharma, Keyur Patel, Bhavana Jamalpur and M. R. Arun
7.1 Introduction
7.1.1 Research Gap
7.1.2 Research Aim
7.2 Literature Review
7.3 Research Methodology
7.3.1 Research Hypothesis
7.3.2 Data Analysis
7.4 Results and Discussion
7.5 Implications
7.6 Conclusion
7.7 Future Research Directions
References
8. Chest X-Ray Analysis for COVID-19 Diagnosis Using an Exascale Computation and Machine Learning Framework
M. Dhinakaran, S. Deivasigamani, Saikat Kar, Nishakar Kankalla, V. Malathy and Saurabh Sharma
8.1 Introduction
8.2 Literature Review
8.3 Research Methodology
8.4 Analysis and Discussion
8.5 Conclusion
References
9. 3D-Printed Human Organ Designs with Tissue Physical Characteristics and Embedded Sensors
A. Chandrashekhar, R. Raffik, R. Sridevi, M. Sindhu, Kodela Rajkumar and Tarun Jaiswal
9.1 Introduction
9.2 Literature Review
9.3 Methodology
9.4 Analysis and Discussion
9.5 Conclusion
References
10. Fast Computing Network Infrastructure for Healthcare Systems Based on 6G Future Perspective
Ranjeet Yadav, S. L. Prathapa Reddy, Akshay Upmanyu, Ravi Kumar Sanapala, V. Malathy and Umakant Bhaskar Gohatre
10.1 Introduction
10.2 Literature Review
10.3 Research Methodology
10.4 Analysis and Discussion
10.5 Conclusion
References
11. Analysis of Multimodality Fusion of Medical Image Segmentation Employing Deep Learning
G. Santhakumar, Dattatray G. Takale, Swati Tyagi, Raju Anitha, Mohit Tiwari and Joshuva Arockia Dhanraj
11.1 Introduction
11.1.1 Research Gap
11.1.2 Research Aim
11.2 Literature Review
11.3 Research Methodology
11.4 Results and Discussion
11.5 Conclusion
References
12. New Perspectives, Challenges, and Advances in Data Fusion in Neuroimaging
Pedada Sujata, Dattatray G. Takale, Swati Tyagi, Saniya Bhalerao, Mohit Tiwari
and Joshuva Arockia Dhanraj
12.1 Introduction
12.1.1 Research Gap
12.2 Literature Review
12.3 Research Methodology
12.3.1 Human Brain Temporal and Spatial Data Mining Using FOCA and Data Fusion
12.3.2 Construction of the Multimodal Neuroimaging Data Fusion
12.4 Results and Discussion
12.4.1 EEG–fMRI Shared Multimodal Simulation Evaluation
12.4.2 Implementation of Multimodal Neuroimaging Data Fusion
12.5 Challenges
12.6 Conclusion
References
13. The Potential of Cloud Computing in Medical Big Data Processing Systems
A. Mallareddy, M. Jaiganesh, Sophia Navis Mary, Manikandan K., Umakant Bhaskar Gohatre and Joshuva Arockia Dhanraj
13.1 Introduction
13.2 Literature Review
13.3 Materials and Method
13.4 Result and Discussion
13.5 Conclusion
References
14. Deep Learning (DL) on Exascale Computing to Speed Up Cancer Investigation
D. Rubidha Devi, S. Ashwini, Samreen Rizvi, P. Venkata Hari Prasad, Mohit Tiwari
and Joshuva Arockia Dhanraj
14.1 Introduction
14.2 Literature Review
14.3 Research Methodology
14.4 Analysis and Discussion
14.5 Conclusion
References
15. Current Breakthroughs and Future Perspectives in Surgery Based on AI-Based Computing Vision
Suneet Gupta, Madhu Kumar Vanteru, Sanjeevkumar Angadi, Manikandan K., Mohit Tiwari and Joshuva Arockia Dhanraj
15.1 Introduction
15.2 Literature Review
15.3 Research Methodology
15.4 Analysis and Discussion
15.5 Conclusion
References
16. MRI-Based Brain Tumor Detection Using Machine Learning
Vivek Kumar, Pinki Chugh, Bhuprabha Bharti, Anchit Bijalwan, Amrendra Tripathi, Ram Narayan and Kapil Joshi
16.1 Introduction
16.2 Pre-Processing
16.3 Segmentation
16.4 Feature Extraction
16.5 SVM Classifier
16.6 Methodology
16.7 Conclusion
References
17. Chili Pepper as a Natural Therapeutic Drug: A Review of Its Anticancer and Antioxidant Properties and Mechanism of Action Using the Machine Learning Approach
Rachana Joshi, Narinder Kumar, B. S. Rawat, Reena Dhyani, Hemlata Sharma and Rajiv Kumar
17.1 Introduction
17.2 Machine Learning Technique
17.3 Composition Profile
17.4 Reactions of Phytochemicals to Drying and Ripening
17.5 Antioxidant Activity
17.6 Anticancer Activity
17.7 Activities that are Anti-Inflammatory and Relieve Pain
17.8 Activities Controlling Diabetes and Hyperglycemia
17.9 The Impacts of Anticholesteremic Activity on Lipid Metabolism
17.10 Anticlotting Effect
17.11 Antimicrobial Activity
17.12 Immune Checkpoint Signaling
17.13 Suppression of Antitumor Immune Response
17.14 Antigen Masking
17.15 Immune-Based Cancer Therapies
17.16 Other Miscellaneous Medicinal Values
17.17 Conclusion
References
18. Exascale Computing: The Next Frontier of High-Performance Computing
Rashmi M., Girija D.K. and Yogeesh N.
18.1 Introduction
18.1.1 Literature Study
18.2 Exascale Computing
18.2.1 Exascale Computers
18.2.2 Case Study
18.2.3 Measuring Computer Speed
18.2.4 Usage of FLOPS in Supercomputers
18.2.5 Exascale Computing: A Crucial Technology
18.2.6 Requirements of High-Speed Computers
18.2.7 Milestones
18.2.8 Exascale Computing Processing
18.2.9 Advantages of Exascale Computing
18.2.10 Exascale Computing in Various Domains
18.2.11 Exascale Computer: A Supercomputer
18.2.12 Exascale Computing Different from Quantum Computing
18.3 Exascale Computing Challenges
18.4 Future Lookup
18.4.1 Needed Improvements
18.5 Conclusion
References
Index
Back to Top
Preface
1. Evaluating the Impact of Healthcare 4.0 on the Performance of Hospitals
Pramod Kumar, Nitu Maurya, Keerthiraj, Somanchi Hari Krishna, Geetha Manoharan
and Anupama Bharti
1.1 Introduction
1.2 Literature Review
1.3 Methodology
1.3.1 Selection of the Sample and Characterization
1.3.2 Creation of a Data-Gathering Tool and Measures
1.3.3 Inspection of the Conceptions’ Reliability and Validity
1.3.4 Data Evaluation
1.4 Result and Discussion
1.5 Conclusion
References
2. Human Breast Cancer Classification Employing the Machine Learning Ensemble
Sreenivas Mekala, S. Srinivasulu Raju, M. Gomathi, Naga Venkateshwara Rao K., Kothandaraman D. and Saurabh Sharma
2.1 Introduction
2.1.1 Breast Cancer Symptoms and Signs
2.1.2 Breast Cancer Risk Factors
2.1.3 Disease Prediction Using Machine Learning
2.2 Literature Review
2.3 Methodology
2.3.1 Bayesian Network
2.3.2 Radial Basis Function
2.3.3 Ensemble Learning
2.3.4 The Suggested Algorithm
2.4 Results and Discussion
2.5 Conclusion
References
3. Multi-Objective Differential Development Using DNN for Multimodality Medical Image Fusion
M. Ranjith Kumar, Abhishek Dondapati, Dilip Kumar Sharma, Prakash Pareek, Rajchandar K. and S. Shalini
3.1 Introduction
3.2 Literature Review
3.3 Methodology
3.3.1 Non-Subsampled Contourlet Transform
3.3.2 Deep Xception Mode Feature Extraction
3.3.3 Differential Evolutions with Several Objectives for Feature Selection
3.3.4 Fusion of High-Frequency Bands
3.4 Result and Discussion
3.4.1 Visual Evaluation
3.4.2 Quantitative Research
3.5 Conclusion
References
4. Multimodal Deep Learning Analysis for Biomedical Data Fusion
Divyanshu Sinha, B. Jogeswara Rao, D. Khalandar Basha, Parvathapuram Pavan Kumar, N. Shilpa and Saurabh Sharma
4.1 Introduction
4.2 Literature Review
4.3 Methodology
4.3.1 Early Fusion
4.3.2 Intermediate Fusion
4.3.3 Late Fusion
4.4 Results and Discussion
4.5 Conclusion
References
5. Developing Robot-Based Neurorehabilitation Exercises Using a Teaching–Training Process
W. Vinu, Sonali Vyas, A. Chandrashekhar, T. Ch. Anil Kumar, T. Raghu and Mohit Tiwari
5.1 Introduction
5.1.1 Research Gap
5.1.2 Research Aim
5.2 Literature Review
5.3 Research Methodology
5.4 Results
5.5 Conclusion
5.6 Future Research Directions
References
6. Investigation on Introduction to Heterogeneous Exascale Computing in the Medical Field
M. Pyingkodi, Raju Shanmugam, Dilip Kumar Sharma, Deepesh Lall, S. Deepan and B. Dasu
6.1 Introduction
6.1.1 Research Gap
6.2 Literature Review
6.3 Research Methodology
6.4 Results and Discussion
6.5 Conclusion
6.6 Future Research Direction
References
7. Adoption of Cloud Computing in the Healthcare Field Using the SEM Approach
R. Chithambaramani, C. Balakumar, Dilip Kumar Sharma, Keyur Patel, Bhavana Jamalpur and M. R. Arun
7.1 Introduction
7.1.1 Research Gap
7.1.2 Research Aim
7.2 Literature Review
7.3 Research Methodology
7.3.1 Research Hypothesis
7.3.2 Data Analysis
7.4 Results and Discussion
7.5 Implications
7.6 Conclusion
7.7 Future Research Directions
References
8. Chest X-Ray Analysis for COVID-19 Diagnosis Using an Exascale Computation and Machine Learning Framework
M. Dhinakaran, S. Deivasigamani, Saikat Kar, Nishakar Kankalla, V. Malathy and Saurabh Sharma
8.1 Introduction
8.2 Literature Review
8.3 Research Methodology
8.4 Analysis and Discussion
8.5 Conclusion
References
9. 3D-Printed Human Organ Designs with Tissue Physical Characteristics and Embedded Sensors
A. Chandrashekhar, R. Raffik, R. Sridevi, M. Sindhu, Kodela Rajkumar and Tarun Jaiswal
9.1 Introduction
9.2 Literature Review
9.3 Methodology
9.4 Analysis and Discussion
9.5 Conclusion
References
10. Fast Computing Network Infrastructure for Healthcare Systems Based on 6G Future Perspective
Ranjeet Yadav, S. L. Prathapa Reddy, Akshay Upmanyu, Ravi Kumar Sanapala, V. Malathy and Umakant Bhaskar Gohatre
10.1 Introduction
10.2 Literature Review
10.3 Research Methodology
10.4 Analysis and Discussion
10.5 Conclusion
References
11. Analysis of Multimodality Fusion of Medical Image Segmentation Employing Deep Learning
G. Santhakumar, Dattatray G. Takale, Swati Tyagi, Raju Anitha, Mohit Tiwari and Joshuva Arockia Dhanraj
11.1 Introduction
11.1.1 Research Gap
11.1.2 Research Aim
11.2 Literature Review
11.3 Research Methodology
11.4 Results and Discussion
11.5 Conclusion
References
12. New Perspectives, Challenges, and Advances in Data Fusion in Neuroimaging
Pedada Sujata, Dattatray G. Takale, Swati Tyagi, Saniya Bhalerao, Mohit Tiwari
and Joshuva Arockia Dhanraj
12.1 Introduction
12.1.1 Research Gap
12.2 Literature Review
12.3 Research Methodology
12.3.1 Human Brain Temporal and Spatial Data Mining Using FOCA and Data Fusion
12.3.2 Construction of the Multimodal Neuroimaging Data Fusion
12.4 Results and Discussion
12.4.1 EEG–fMRI Shared Multimodal Simulation Evaluation
12.4.2 Implementation of Multimodal Neuroimaging Data Fusion
12.5 Challenges
12.6 Conclusion
References
13. The Potential of Cloud Computing in Medical Big Data Processing Systems
A. Mallareddy, M. Jaiganesh, Sophia Navis Mary, Manikandan K., Umakant Bhaskar Gohatre and Joshuva Arockia Dhanraj
13.1 Introduction
13.2 Literature Review
13.3 Materials and Method
13.4 Result and Discussion
13.5 Conclusion
References
14. Deep Learning (DL) on Exascale Computing to Speed Up Cancer Investigation
D. Rubidha Devi, S. Ashwini, Samreen Rizvi, P. Venkata Hari Prasad, Mohit Tiwari
and Joshuva Arockia Dhanraj
14.1 Introduction
14.2 Literature Review
14.3 Research Methodology
14.4 Analysis and Discussion
14.5 Conclusion
References
15. Current Breakthroughs and Future Perspectives in Surgery Based on AI-Based Computing Vision
Suneet Gupta, Madhu Kumar Vanteru, Sanjeevkumar Angadi, Manikandan K., Mohit Tiwari and Joshuva Arockia Dhanraj
15.1 Introduction
15.2 Literature Review
15.3 Research Methodology
15.4 Analysis and Discussion
15.5 Conclusion
References
16. MRI-Based Brain Tumor Detection Using Machine Learning
Vivek Kumar, Pinki Chugh, Bhuprabha Bharti, Anchit Bijalwan, Amrendra Tripathi, Ram Narayan and Kapil Joshi
16.1 Introduction
16.2 Pre-Processing
16.3 Segmentation
16.4 Feature Extraction
16.5 SVM Classifier
16.6 Methodology
16.7 Conclusion
References
17. Chili Pepper as a Natural Therapeutic Drug: A Review of Its Anticancer and Antioxidant Properties and Mechanism of Action Using the Machine Learning Approach
Rachana Joshi, Narinder Kumar, B. S. Rawat, Reena Dhyani, Hemlata Sharma and Rajiv Kumar
17.1 Introduction
17.2 Machine Learning Technique
17.3 Composition Profile
17.4 Reactions of Phytochemicals to Drying and Ripening
17.5 Antioxidant Activity
17.6 Anticancer Activity
17.7 Activities that are Anti-Inflammatory and Relieve Pain
17.8 Activities Controlling Diabetes and Hyperglycemia
17.9 The Impacts of Anticholesteremic Activity on Lipid Metabolism
17.10 Anticlotting Effect
17.11 Antimicrobial Activity
17.12 Immune Checkpoint Signaling
17.13 Suppression of Antitumor Immune Response
17.14 Antigen Masking
17.15 Immune-Based Cancer Therapies
17.16 Other Miscellaneous Medicinal Values
17.17 Conclusion
References
18. Exascale Computing: The Next Frontier of High-Performance Computing
Rashmi M., Girija D.K. and Yogeesh N.
18.1 Introduction
18.1.1 Literature Study
18.2 Exascale Computing
18.2.1 Exascale Computers
18.2.2 Case Study
18.2.3 Measuring Computer Speed
18.2.4 Usage of FLOPS in Supercomputers
18.2.5 Exascale Computing: A Crucial Technology
18.2.6 Requirements of High-Speed Computers
18.2.7 Milestones
18.2.8 Exascale Computing Processing
18.2.9 Advantages of Exascale Computing
18.2.10 Exascale Computing in Various Domains
18.2.11 Exascale Computer: A Supercomputer
18.2.12 Exascale Computing Different from Quantum Computing
18.3 Exascale Computing Challenges
18.4 Future Lookup
18.4.1 Needed Improvements
18.5 Conclusion
References
Index
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