- First book to focus on deep learning-based approaches in the field of cancer diagnostics.
- Covers the state of the art across a wide-range of topics.
- Topics include preprocessing data, prediction of cancer susceptibility and reoccurence, detection of different cancers, complexity and challenges.
Edited by:
Jyotismita Chaki,
Aysegul Ucar
Imprint: CRC Press
Country of Publication: United Kingdom
Dimensions:
Height: 234mm,
Width: 156mm,
Weight: 440g
ISBN: 9781032233857
ISBN 10: 1032233850
Pages: 167
Publication Date: 22 February 2023
Audience:
College/higher education
,
Professional and scholarly
,
Primary
,
Undergraduate
Format: Hardback
Publisher's Status: Active
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
Jyotismita Chaki, PhD, is an Associate Professor at School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India. Aysegul Ucar, PhD, is a Professor in Department of Mechatronics Engineering, Firat University, Turkey.