Current Applications of Deep Learning in Cancer Diagnostics

Title: Current Applications of Deep Learning in Cancer Diagnostics
Author: Aysegul Ucar, Jyotismita Chaki
ISBN: 1032223197 / 9781032223193
Format: Soft Cover
Pages: 187
Publisher: CRC Press
Year: 2024
Availability: 15-30 days

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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.

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Chapter 1 : Contemporary Trends in the Early Detection and Diagnosis of Human Cancers Using Deep Learning Techniques
Chapter 2 : Cancer Data Pre-Processing Techniques
Chapter 3 : A Survey on Deep Learning Techniques for Breast, Leukemia and Cervical Cancer Prediction
Chapter 4 : An Optimized Deep Learning Technique for Detecting Lung Cancer from CT Images
Chapter 5 : Brain Tumor Segmentation Utilizing MRI Multimodal Images with Deep Learning
Chapter 6 : Detection and Classification of Brain Tumors Using Light-Weight Convolutional Neural Network
Chapter 7 : Parallel Dense Skip Connected CNN Approach for Brain Tumor Classification
Chapter 8 : Liver Tumor Segmentation Using Deep Learning Neural Networks
Chapter 9 : Deep Learning Algorithms for Classification and Prediction of Acute Lymphoblastic Leukemia
Chapter 10 : Cervical Pap Smear Screening and Cancer Detection Using Deep Neural Network
Chapter 11 : Cancer Detection Using Deep Neural Network: Differentiation of Squamous Carcinoma Cells in Oral Pathology
Chapter 12 : Challenges and Future Scopes in Current Applications of Deep Learning in Human Cancer Diagnostics