Deep Learning for Medical Applications with Unique Data informs readers about the most recent deep learning-based medical applications in which only unique data gathered in real cases are used. The book provides examples of how deep learning can be used in different problem areas and frameworks in both clinical and research settings, including medical image analysis, medical image registration, time series analysis, medical data synthesis, drug discovery, and pre-processing operations. The volume discusses not only positive findings, but also negative ones obtained by deep learning techniques, including the use of newly developed deep learning techniques rarely reported in the existing literature. The book excludes research works with ready data sets and includes only unique data use to better understand the state of deep learning in real-world cases, along with the feedback and user experiences from physicians and medical staff for applied deep learning-based solutions. Other applications presented in the book include hybrid solutions with deep learning support, disease diagnosis with deep learning focusing on rare diseases and cancer, patient care and treatment, genomics research, as well as research on robotics and autonomous systems.
- Introduces deep learning, demonstrating concepts for a wide variety of medical applications using unique data, excluding research with ready datasets
- Encompasses a wide variety of biomedical applications, including unsupervised learning, natural language processing, pattern recognition, image and video processing and disease diagnosis
- Provides a robust set of methods that will help readers appropriately and judiciously use the most suitable deep learning techniques for their applications
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Techn.
Dateigröße
ISBN-13
978-0-12-824146-2 (9780128241462)
Schweitzer Klassifikation
1. A deep learning approach for the prediction of heart attacks based on data analysis2. A comparative study on fully convolutional networks-FCN-8, FCN-16, and FCN-32: A case of brain tumor3. Deep learning applications for disease diagnosis4. An artificial intelligent cognitive approach for classification and recognition of white blood cells employing deep learning for medical applications5. Deep learning on medical image analysis on COVID-19 x-ray dataset using an X-Net architecture6. Early prediction of heart disease using a deep learning approach7. Machine learning and deep learning algorithms in disease prediction: Future trends for the healthcare system8. Automatic detection of white matter hyperintensities via mask region-based convolutional neural networks using magnetic resonance images9. Diagnosing glaucoma with optic disk segmenting and deep learning from color retinal fundus images10. An artificial intelligence framework to ensure a trade-off between sanitary and economic perspectives during the COVID-19 pandemic11. Prediction of COVID-19 using machine learning techniques
