Handbook of Deep Learning in Biomedical Engineering and Health Informatics
This new volume discusses state-of-the-art deep learning techniques and approaches that can be applied in biomedical systems and health informatics. Deep learning in the biomedical field is an effective method of collecting and analyzing data that can be used for the accurate diagnosis of disease.
This volume delves into a variety of applications, techniques, algorithms, platforms, and tools used in this area, such as image segmentation, classification, registration, and computer-aided analysis. The editors proceed on the principle that accurate diagnosis of disease depends on image acquisition and interpretation. There are many methods to get high resolution radiological images, but we are still lacking in automated image interpretation. Currently deep learning techniques are providing a feasible solution for automatic diagnosis of disease with good accuracy. Analyzing clinical data using deep learning techniques enables clinicians to diagnose diseases at an early stage and treat patients more effectively.
Chapters explore such approaches as deep learning algorithms, convolutional neural networks and recurrent neural network architecture, image stitching techniques, deep RNN architectures, and more. This volume also depicts how deep learning techniques can be applied for medical diagnostics of several specific health scenarios, such as cancer, COVID-19, acute neurocutaneous syndrome, cardiovascular and neuro diseases, skin lesions and skin cancer, etc.
- Introduces important recent technological advancements in the field
- Describes the various techniques, platforms, and tools used in biomedical deep learning systems
- Includes informative case studies that help to explain the new technologies
Handbook of Deep Learning in Biomedical Engineering and Health Informatics provides a thorough exploration of biomedical systems applied with deep learning techniques and will provide valuable information for researchers, medical and industry practitioners, academicians, and students.
1. Review of Existing Systems in Biomedical Using Deep Learning Algorithms
M. Sowmiya, C. Thilagavathi, M. Rajeswari, R. Divya, K. Anusree, and Shyam Krishna
2. An Overview of Convolutional Neural Network Architecture and Its Variants in Medical Diagnostics of Cancer and COVID-19
M. Pavithra, R. Rajmohan, T. Ananth Kumar, and S. G. Sandhya
3. Technical Assessment of Various Image Stitching Techniques: A Deep Learning Approach
M. Anly Antony, R. Satheesh Kumar, G. R. Gnana King, V. Yuvaraj, and Chidambaranathan
4. CCNN: A Deep Learning Approach for an Acute Neurocutaneous Syndrome via Cloud-Based MRI Images
S. Arunmozhi Selvi, T. Ananth Kumar, and R. S. Rajesh
5. Critical Investigation and Prototype Study on Deep Brain Stimulations: An Application of Biomedical Engineering in Healthcare
V. Milner Paul, S. R. Boselin Prabhu, T. Jarin, and T. Ananth Kumar
6. Insight into Various Algorithms for Medical Image Analyzes Using Convolutional Neural Networks (Deep Learning)
S. Sundaresan, K. Suresh Kumar, V. Kishore Kumar, and A. Jayakumar
7. Exploration of Deep RNN Architectures: LSTM and GRU in Medical Diagnostics of Cardiovascular and Neuro Diseases
R. Rajmohan, M. Pavithra, T. Ananth Kumar, and P. Manjubala
8. Medical Image Classification and Manifold Disease Identification Through Convolutional Neural Networks: A Research Perspective
K. Suresh Kumar, A. S. Radhamani, S. Sundaresan, and T. Ananth Kumar
9. Melanoma Detection on Skin Lesion Images Using K-Means Algorithm and SVM Classifier
M. Julie Therese, A. Devi, and G. Kavya
10. Role of Deep Learning Techniques in Detecting Skin Cancer: A Review
S. M. Jaisakthi and B. Devikirubha
11. Deep Learning and Its Applications in Biomedical Image Processing
V. V. Satyanarayana Tallapragada