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.

Table of Contents:
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