Current Applications of Deep Learning in Cancer Diagnostics (eBook, ePUB)

Redaktion: Chaki, Jyotismita; Ucar, Aysegul

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 187
• Erscheinungstermin: 22. Februar 2023
• Englisch
• ISBN-13: 9781000836196
• Artikelnr.: 67165442

Autorenporträt

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.

Inhaltsangabe

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