| ISBN13: | 9781032233857 |
| ISBN10: | 10322338511 |
| Binding: | Hardback |
| No. of pages: | 187 pages |
| Size: | 234x156 mm |
| Weight: | 440 g |
| Language: | English |
| Illustrations: | 52 Illustrations, black & white; 27 Illustrations, color; 21 Halftones, black & white; 14 Halftones, color; 28 Line drawings, black & white; 16 Line drawings, color; 19 Tables, black & white |
| 581 |
Medicine in general
Clinical medicine and internal medicine in general
Radiology, imaging, nuclear medicine
Oncology, cancer
Engineering in general
Electrical engineering and telecommunications, precision engineering
Energy industry
Theory of computing, computing in general
Data management in computer systems
System organization
Computer architecture, logic design
Operating systems and graphical user interfaces
Software development
Digital signal, audio and image processing
Safety and health aspects of computing
Environmental sciences
Medical biotechnology
Product design
Medicine in general (charity campaign)
Clinical medicine and internal medicine in general (charity campaign)
Radiology, imaging, nuclear medicine (charity campaign)
Oncology, cancer (charity campaign)
Engineering in general (charity campaign)
Electrical engineering and telecommunications, precision engineering (charity campaign)
Energy industry (charity campaign)
Theory of computing, computing in general (charity campaign)
Data management in computer systems (charity campaign)
System organization (charity campaign)
Computer architecture, logic design (charity campaign)
Operating systems and graphical user interfaces (charity campaign)
Software development (charity campaign)
Digital signal, audio and image processing (charity campaign)
Safety and health aspects of computing (charity campaign)
Environmental sciences (charity campaign)
Medical biotechnology (charity campaign)
Product design (charity campaign)
Current Applications of Deep Learning in Cancer Diagnostics
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This book demonstrates the core concepts of deep learning algorithms that, using diagrams, data tables, and examples, are especially useful for deep learning based human cancer diagnostics.
This book examines deep learning-based approaches in the field of cancer diagnostics, as well as pre-processing techniques, which are essential to cancer diagnostics. Topics include introduction to current applications of deep learning in cancer diagnostics, pre-processing of cancer data using deep learning, review of deep learning techniques in oncology, overview of advanced deep learning techniques in cancer diagnostics, prediction of cancer susceptibility using deep learning techniques, prediction of cancer reoccurrence using deep learning techniques, deep learning techniques to predict the grading of human cancer, different human cancer detection using deep learning techniques, prediction of cancer survival using deep learning techniques, complexity in the use of deep learning in cancer diagnostics, and challenges and future scopes of deep learning techniques in oncology.
1. Contemporary Trends in the Early Detection and Diagnosis of Human Cancers Using Deep Learning Techniques, 2. Cancer Data Pre-Processing Techniques, 3. A Survey on Deep Learning Techniques for Breast, Leukemia and Cervical Cancer Prediction, 4. An Optimized Deep Learning Technique for Detecting Lung Cancer from CT Images, 5. Brain Tumor Segmentation Utilizing MRI Multimodal Images with Deep Learning, 6. Detection and Classification of Brain Tumors Using Light-Weight Convolutional Neural Network, 7. Parallel Dense Skip Connected CNN Approach for Brain Tumor Classification, 8. Liver Tumor Segmentation Using Deep Learning Neural Networks, 9. Deep Learning Algorithms for Classification and Prediction of Acute Lymphoblastic Leukemia, 10. Cervical Pap Smear Screening and Cancer Detection Using Deep Neural Network, 11. Cancer Detection Using Deep Neural Network: Differentiation of Squamous Carcinoma Cells in Oral Pathology, 12. Challenges and Future Scopes in Current Applications of Deep Learning in Human Cancer Diagnostics
