| ISBN13: | 9781032622606 |
| ISBN10: | 1032622601 |
| Binding: | Hardback |
| No. of pages: | 274 pages |
| Size: | 234x156 mm |
| Weight: | 666 g |
| Language: | English |
| Illustrations: | 60 Illustrations, black & white; 42 Illustrations, color; 17 Halftones, black & white; 14 Halftones, color; 43 Line drawings, black & white; 28 Line drawings, color; 22 Tables, black & white |
| 700 |
Deep Learning in Biomedical Signal and Medical Imaging
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This book offers detailed information on biomedical imaging using Deep Convolutional Neural Networks (Deep CNN). It focuses on different types of biomedical images to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis, and image processing perspectives.
This book offers detailed information on biomedical imaging using Deep Convolutional Neural Networks (Deep CNN). It focuses on different types of biomedical images to enable readers to understand the effectiveness and the potential. It includes topics such as disease diagnosis and image processing perspectives.
Deep Learning in Biomedical Signal and Medical Imaging discusses classification, segmentation, detection, tracking, and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT, and X-RAY, amongst others. It surveys the most recent techniques and approaches in this field, with both broad coverage and enough depth to be of practical use to working professionals. It includes examples of the application of signal and image processing employing Deep CNN to Alzheimer?s, brain tumor, skin cancer, breast cancer, and stroke prediction, as well as ECG and EEG signals. This book offers enough fundamental and technical information on these techniques, approaches, and related problems without overcrowding the reader?s head. It presents the results of the latest investigations in the field of Deep CNN for biomedical data analysis. The techniques and approaches presented in this book deal with the most important and/or the newest topics encountered in this field. They combine the fundamental theory of artificial intelligence (AI), machine learning (ML,) and Deep CNN with practical applications in biology and medicine. Certainly, the list of topics covered in this book is not exhaustive, but these topics will shed light on the implications of the presented techniques and approaches on other topics in biomedical data analysis.
The book is written for graduate students, researchers, and professionals in biomedical engineering, electrical engineering, signal process engineering, biomedical imaging, and computer science. The specific and innovative solutions covered in this book for both medical and biomedical applications are critical to scientists, researchers, practitioners, professionals, and educators who are working in the context of the topics.
1. Detection of Diabetic Retinopathy from retinal fundus images by using CNN Model ResNet-50.
2. DNASNet-RF: Automated Deep NAS-network with Random Forest for Classifying and Detecting Multi-class Brain Tumor. 3. Deep CNNs in image-guided diagnosis of breast and skin cancers. 4. Robust Learning Principle Design to Detect Diabetic Retinopathy Disease in Early Stages with Skilled Feature Extraction Policy. 5. Liver Tumour Detection using Machine Learning Techniques: A Systematic Review. 6. Deep Learning in Photoacoustic Tomographic Image Reconstruction. 7. Design and Development of Computer Aided Diagnosis to Detect Lung Cancer Disease by Using Intelligent Deep Learning Principle. 8. Novel Methodology to Predict and Classify Liver Diseases Based on Hybrid Deep Learning Strategy. 9. Improvements in Analysing Biomedical Signals and Medical Images Using Deep Learning. 10. A Survey on Lung Cancer Diagnosis Using Deep Learning Techniques. 11. Content-Based Medical Image Retrieval using CNN Feature Extraction and Hashing Dimensionality Reduction. 12. Experimental Evaluation of Deep Learning-Assisted Brain Tumor Identification with Advanced Classification Methodology. 13. Study of Biomedical Segmentation Based On Recent Techniques and Deep Learning. 14. Deep CNN in Healthcare. 15. An Improved Multi-Class Breast Cancer Classification and Abnormality Detection Based On Modified Deep Learning Neural Network Principles
