Emerging Trends and Applications of Deep Learning for Biomedical Data Analysis
- 1st Edition - May 2, 2025
- Imprint: Academic Press
- Editors: Smita Sharma, Balamurugan Balusamy, S. Ramesh, Ali Kashif Bashir
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 6 7 6 5 - 9
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 6 7 6 6 - 6
Emerging Trends and Applications of Deep Learning for Biomedical Data Analysis introduces the latest emerging trends and applications of deep learning in biomedical data analys… Read more
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Emerging Trends and Applications of Deep Learning for Biomedical Data Analysis introduces the latest emerging trends and applications of deep learning in biomedical data analysis. This book delves into various use cases where deep learning is applied in industrial, social, and personal contexts within the biomedical domain. By gaining a comprehensive understanding of deep learning in biomedical data analysis, readers will develop the skills to critically evaluate research papers, methodologies, and emerging trends. In 11 chapters, this book provides insights into the fundamentals of the latest research trends in the applications of deep learning in biosciences. With several case studies and use cases, it familiarizes the reader with a comprehensive understanding of deep learning algorithms, architectures, and methodologies speci cally applicable to biomedical data analysis. This title is an ideal reference for researchers across the biomedical sciences.
● Provides a succinct overview of the cutting-edge technologies that are altering disease diagnosis, patient monitoring, and medical research
● Bridges the gap between biomedical engineering and deep learning by providing a comprehensive resource for comprehending the intersection of these disciplines
● Investigates how deep learning may change healthcare by providing new insights, diagnostics, and treatments via intelligent biomedical systems
● Bridges the gap between biomedical engineering and deep learning by providing a comprehensive resource for comprehending the intersection of these disciplines
● Investigates how deep learning may change healthcare by providing new insights, diagnostics, and treatments via intelligent biomedical systems
Researchers working across the biosciences in the fields of artificial intelligence, deep learning and biomedical engineering
1. Deep learning, artificial intelligence, and bioinformatics promises innovations and imminent forecasts in SARS-COVID-19 genome data analysis
S. Sheik Asraf, P. Nagaraj, and V. Muneeswaran
1.1 Introduction
1.2 COVID-19—a global pandemic
1.3 Genomics of COVID-19
1.4 Applications of deep learning in COVID-19 genomics studies
1.5 Role of artificial intelligence in COVID-19 genomics research
1.6 Usage of bioinformatics tools, software, and databases in COVID-19 genomics investigation
1.7 Challenges and prospects of deep learning, artificial intelligence, and bioinformatics in COVID-19 genomics
1.8 Conclusion
References
2. Integration of IoT and AI for potato leaf disease detection: enhancing agricultural efficiency and sustainability
E. Senthamil Selvi and S. Anusuya
2.1 Introduction
2.2 Literature survey
2.3 Classification process for potato leaf diseases
2.4 Image preliminary processing
2.5 Image augmentation
2.6 Feature extraction
2.7 Evaluation and recognition
2.8 Methods and materials
2.9 Transfer learning
2.10 Pretrained network model
2.11 Proposed model
2.12 Result and discussion
2.13 Conclusion
2.14 Future work
References
3. A hybridized long–short-term memory networks-based deep learning model using reptile search optimization for COVID-19 prediction
Balakrishnama Manohar, Raja Das, Potharla Ramadevi, and Balamurugan Balusamy
3.1 Introduction
3.2 Materials and methods
3.3 Data preprocessing
3.4 Data normalization
3.5 Proposed methodology
3.6 Methodology
3.7 Reptile search algorithm
3.8 Encircling phase (global search or exploration)
3.9 Hunting phase (local search or exploitation)
3.10 Optimized long–short-term memory networks-reptile search algorithm model
3.11 Model evaluation
3.12 Results
3.13 Conclusion
References
4. Improving coronavirus classification accuracy with transfer learning and chest radiograph analysis
M. Lakshmi, Raja Das, Balakrishnama Manohar, and Balamurugan Balusamy
4.1 Introduction
4.2 Related works
4.3 Materials and methods
4.4 Results and discussion
4.5 Conclusion
References
5. A hybrid deep neural network using the Levenberg–Marquardt algorithm applied to the nonlinear magnetohydrodynamic Jeffery–Hamel blood flow problem
Priyanka Chandra, Raja Das, and Smita Sharma
5.1 Introduction
5.2 Mathematical modeling
5.3 Solution methodology
5.4 Result and discussion
5.5 Conclusion
Ethical statement
Acknowledgment
Declaration of interest statement
Funding
Data availability statement
References
6. An image segmentation method using intuitionistic fuzzy k-means and convolutional neural networks in multiclass image classification
Potharla Ramadevi, Raja Das, M. Lakshmi, Balakrishnama Manohar, and Smita Sharma
6.1 Introduction
6.2 Related works
6.3 Methodology
6.4 Results and discussion
6.5 Conclusion
References
7. Deep learning for wearable sensor data analysis
P. Aakash Kumar, Abha Rani, S. Amutha, and B. Surendiran
7.1 Introduction
7.2 Literature review
7.3 Methodology
7.4 Result and discussion
7.5 Conclusion
References
8. Unveiling emotions in real-time: a novel approach to face emotion recognition
Gowthami V. and Vijayalakshmi R.
8.1 Introduction
8.2 Convolutional neural network
8.3 Objective
8.4 Literature survey
8.5 Proposed work
8.6 Pseudocode for training the model
8.7 Results
8.8 Future work
References
Further reading
9. Unleashing the power of convolutional neural networks for diabetic retinopathy detection in ophthalmology
Gowthami V. and K. Alamelu
9.1 Introduction
9.2 Literature review
9.3 System methodology
9.4 Result and discussion
9.5 Conclusion and future work
References
10. Case studies and use cases of deep learning for biomedical applications
Amutha Prabakar Muniyandi, Padmavathy T., and Balamurugan Balusamy
10.1 Introduction
10.2 Impact of deep learning in bio-engineering
10.3 Evolution of artificial neural networks
10.4 Applications of deep learning—bioinformatics
10.5 Explainable artificial intelligence in bioinformatics
10.6 Conclusion
References
11. A convolutional neural network-based deep ensemble method for computed tomography scan image-based lung cancer diagnosis
R. Jothi, Shravani Swaroop Urala, and K. Muthukumaran
11.1 Introduction
11.2 Related work
11.3 Dataset
11.4 Methodology
11.5 Experimental results and discussion
11.6 Conclusion
References
Index
S. Sheik Asraf, P. Nagaraj, and V. Muneeswaran
1.1 Introduction
1.2 COVID-19—a global pandemic
1.3 Genomics of COVID-19
1.4 Applications of deep learning in COVID-19 genomics studies
1.5 Role of artificial intelligence in COVID-19 genomics research
1.6 Usage of bioinformatics tools, software, and databases in COVID-19 genomics investigation
1.7 Challenges and prospects of deep learning, artificial intelligence, and bioinformatics in COVID-19 genomics
1.8 Conclusion
References
2. Integration of IoT and AI for potato leaf disease detection: enhancing agricultural efficiency and sustainability
E. Senthamil Selvi and S. Anusuya
2.1 Introduction
2.2 Literature survey
2.3 Classification process for potato leaf diseases
2.4 Image preliminary processing
2.5 Image augmentation
2.6 Feature extraction
2.7 Evaluation and recognition
2.8 Methods and materials
2.9 Transfer learning
2.10 Pretrained network model
2.11 Proposed model
2.12 Result and discussion
2.13 Conclusion
2.14 Future work
References
3. A hybridized long–short-term memory networks-based deep learning model using reptile search optimization for COVID-19 prediction
Balakrishnama Manohar, Raja Das, Potharla Ramadevi, and Balamurugan Balusamy
3.1 Introduction
3.2 Materials and methods
3.3 Data preprocessing
3.4 Data normalization
3.5 Proposed methodology
3.6 Methodology
3.7 Reptile search algorithm
3.8 Encircling phase (global search or exploration)
3.9 Hunting phase (local search or exploitation)
3.10 Optimized long–short-term memory networks-reptile search algorithm model
3.11 Model evaluation
3.12 Results
3.13 Conclusion
References
4. Improving coronavirus classification accuracy with transfer learning and chest radiograph analysis
M. Lakshmi, Raja Das, Balakrishnama Manohar, and Balamurugan Balusamy
4.1 Introduction
4.2 Related works
4.3 Materials and methods
4.4 Results and discussion
4.5 Conclusion
References
5. A hybrid deep neural network using the Levenberg–Marquardt algorithm applied to the nonlinear magnetohydrodynamic Jeffery–Hamel blood flow problem
Priyanka Chandra, Raja Das, and Smita Sharma
5.1 Introduction
5.2 Mathematical modeling
5.3 Solution methodology
5.4 Result and discussion
5.5 Conclusion
Ethical statement
Acknowledgment
Declaration of interest statement
Funding
Data availability statement
References
6. An image segmentation method using intuitionistic fuzzy k-means and convolutional neural networks in multiclass image classification
Potharla Ramadevi, Raja Das, M. Lakshmi, Balakrishnama Manohar, and Smita Sharma
6.1 Introduction
6.2 Related works
6.3 Methodology
6.4 Results and discussion
6.5 Conclusion
References
7. Deep learning for wearable sensor data analysis
P. Aakash Kumar, Abha Rani, S. Amutha, and B. Surendiran
7.1 Introduction
7.2 Literature review
7.3 Methodology
7.4 Result and discussion
7.5 Conclusion
References
8. Unveiling emotions in real-time: a novel approach to face emotion recognition
Gowthami V. and Vijayalakshmi R.
8.1 Introduction
8.2 Convolutional neural network
8.3 Objective
8.4 Literature survey
8.5 Proposed work
8.6 Pseudocode for training the model
8.7 Results
8.8 Future work
References
Further reading
9. Unleashing the power of convolutional neural networks for diabetic retinopathy detection in ophthalmology
Gowthami V. and K. Alamelu
9.1 Introduction
9.2 Literature review
9.3 System methodology
9.4 Result and discussion
9.5 Conclusion and future work
References
10. Case studies and use cases of deep learning for biomedical applications
Amutha Prabakar Muniyandi, Padmavathy T., and Balamurugan Balusamy
10.1 Introduction
10.2 Impact of deep learning in bio-engineering
10.3 Evolution of artificial neural networks
10.4 Applications of deep learning—bioinformatics
10.5 Explainable artificial intelligence in bioinformatics
10.6 Conclusion
References
11. A convolutional neural network-based deep ensemble method for computed tomography scan image-based lung cancer diagnosis
R. Jothi, Shravani Swaroop Urala, and K. Muthukumaran
11.1 Introduction
11.2 Related work
11.3 Dataset
11.4 Methodology
11.5 Experimental results and discussion
11.6 Conclusion
References
Index
- Edition: 1
- Published: May 2, 2025
- Imprint: Academic Press
- Language: English
SS
Smita Sharma
Dr. Smita Sharma is a Senior Member of IEEE and currently serves as the WIE Chair of the IEEE Uttar Pradesh Section. She holds a Ph.D. in Wireless Body Area Sensor Networks from Uttarakhand Technical University, a Central Government University, along with a B.Tech from Galgotias College of Engineering and an M.Tech from Madan Mohan Malviya Engineering College, Uttar Pradesh, specializing in Electronics and Communication Engineering. Dr. Sharma is associated with the National Institute of Electronics & Information Technology (NIELIT), New Delhi. Previously, she was an Associate Professor at Amity University, Uttar Pradesh, where she dedicated 14 years to teaching and research. With a remarkable academic and research portfolio, Dr. Sharma has authored over 50 peer-reviewed articles published in prestigious international journals and conferences. She has contributed to numerous book chapters, edited several books, and holds multiple Indian patents. She actively collaborates with distinguished professors from globally renowned QS-ranked universities and plays a key role in organizing IEEE conferences. Dr. Sharma’s research focuses on cutting-edge areas including the Internet of Things (IoT), wireless sensor networks (WSNs), network security, artificial intelligence, and machine learning. Within WSNs, her work emphasizes improving network efficiency and extending sensor lifespan. A dedicated contributor to the academic community, Dr. Sharma serves as a reviewer for leading journals and conferences, is a sought-after speaker at global events, and is an integral part of publication teams for internationally recognized journals. She is also an active member of IAENG and CSI societies, promoting diversity, inclusion, and technological advancement.
Affiliations and expertise
National Institute of Electronics & Information Technology (NIELIT), New Delhi, IndiaBB
Balamurugan Balusamy
Dr. Balamurugan Balusamy is currently working as an Associate Dean Student in Shiv Nadar Institution of Eminence, Delhi-NCR. He is part of the Top 2% Scientists Worldwide 2023 by Stanford University in the area of Data Science/AI/ML. He is also an Adjunct Professor in the Department of Computer Science and Information Engineering, Taylor University, Malaysia. His contributions focus on engineering education, blockchain, and data sciences
Affiliations and expertise
Shiv Nadar University, Delhi-NCR, IndiaSR
S. Ramesh
Dr. S. Ramesh is an Assistant Professor (SS) in the Department of Mechatronics Engineering, Rajalakshmi Engineering College, Thandalam, Chennai. He has completed his Ph.D. degree in Embedded Systems/ Machine Learning from VIT University, Chennai in 2020, an M. Tech. Degree in Embedded Systems from SRM University, Chennai, Tamilnadu, India in 2011, B.E. Degree from National Engineering College, Kovilpatti, Tamilnadu, India in 2008. In addition to this, he is currently doing Post Doctoral Research in Malaysia. He has over 13 years of Teaching and Research Experience at various Universities and Engineering Colleges around India.
Affiliations and expertise
Rajalakshmi Engineering College, ChennaiAB
Ali Kashif Bashir
Ali Kashif Bashir is an Associate Professor at the School of Computing and Mathematics of Manchester Metropolitan University, United Kingdom, an Adjunct Professor at the School of Electrical Engineering and Computer Science at the National University of Science and Technology, Islamabad (NUST), Pakistan, an Honorary Professor at the School of Information and Communication Engineering of the University of Electronics Science and Technology of China (UESTC), and a Chief Advisor at the Visual Intelligence Research Center, UESTC, China. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), United States and a distinguished speaker of the Association for Computing Machinery (ACM), United States.
Affiliations and expertise
Metropolitan University, UKRead Emerging Trends and Applications of Deep Learning for Biomedical Data Analysis on ScienceDirect