Current Applications of Deep Learning in Cancer Diagnostics (PDF)
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...
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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.
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.
- 2023, 1. Auflage, 187 Seiten, Englisch
- Herausgegeben: Jyotismita Chaki, Aysegul Ucar
- Verlag: Taylor & Francis
- ISBN-10: 1000836150
- ISBN-13: 9781000836158
- Erscheinungsdatum: 22.02.2023
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- Dateiformat: PDF
- Größe: 15 MB
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