Current Applications of Deep Learning in Cancer Diagnostics
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39 976 Ft
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Product details:
- Edition number 1
- Publisher CRC Press
- Date of Publication 22 February 2023
- ISBN 9781032233857
- Binding Hardback
- No. of pages187 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 481
Categories
Short description:
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.
MoreLong description:
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.
MoreTable of Contents:
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
More
Current Applications of Deep Learning in Cancer Diagnostics
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