Deep Learning for Medical Applications with Unique Data

Examines the concepts and technical methods of deep learning for a wide variety of clinical and research applications using unique data.

Deepak Gupta (Edited by), Utku Kose (Edited by), Ashish Khanna (Edited by), Valentina Emilia Balas (Edited by)

9780128241455, Elsevier Science

Paperback, published 17 February 2022

256 pages, 60 illustrations (20 in full color)
23.5 x 19 x 1.7 cm, 0.57 kg

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.

1. A deep learning approach for the prediction of heart attacks based on data analysis 2. A comparative study on fully convolutional networks—FCN-8, FCN-16, and FCN-32: A case of brain tumor 3. Deep learning applications for disease diagnosis 4. An artificial intelligent cognitive approach for classification and recognition of white blood cells employing deep learning for medical applications 5. Deep learning on medical image analysis on COVID-19 x-ray dataset using an X-Net architecture 6. Early prediction of heart disease using a deep learning approach 7. Machine learning and deep learning algorithms in disease prediction: Future trends for the healthcare system 8. Automatic detection of white matter hyperintensities via mask region-based convolutional neural networks using magnetic resonance images 9. Diagnosing glaucoma with optic disk segmenting and deep learning from color retinal fundus images 10. An artificial intelligence framework to ensure a trade-off between sanitary and economic perspectives during the COVID-19 pandemic 11. Prediction of COVID-19 using machine learning techniques

Subject Areas: Biomedical engineering [MQW]