Ulteriori informazioni
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.
Sommario
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
Info autore
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.
Riassunto
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.
