Current Applications of Deep Learning in Cancer Diagnostics
Editat de Jyotismita Chaki, Aysegul Ucaren Limba Engleză Paperback – 9 oct 2024
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Specificații
ISBN-13: 9781032223193
ISBN-10: 1032223197
Pagini: 187
Ilustrații: 158
Dimensiuni: 156 x 234 mm
Greutate: 0.35 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press
Locul publicării:Boca Raton, United States
ISBN-10: 1032223197
Pagini: 187
Ilustrații: 158
Dimensiuni: 156 x 234 mm
Greutate: 0.35 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press
Locul publicării:Boca Raton, United States
Public țintă
Academic, Postgraduate, and ProfessionalCuprins
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
Notă biografică
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.
Aysegul Ucar, PhD, is a Professor in Department of Mechatronics Engineering, Firat University, Turkey.
Descriere
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.