Current Applications of Deep Learning in Cancer Diagnostics

This book provides an comprehensive overview of deep learning-based approaches in cancer diagnostics, covering preprocessing data, prediction of cancer susceptibility and reoccurence, detection of different cancers, and complexity and challenges.

Format: Hardback
Length: 167 pages
Publication date: 22 February 2023
Publisher: Taylor & Francis Ltd

The groundbreaking work "Deep Learning Approaches in Cancer Diagnostics" is a pioneering text that delves into the realm of cutting-edge technologies in cancer diagnostics, focusing specifically on deep learning-based methodologies. This comprehensive volume encompasses a wide spectrum of topics, providing a comprehensive overview of the state-of-the-art approaches employed in this field.

The book begins by exploring the essential aspects of preprocessing data, a crucial step in ensuring the accuracy and reliability of deep learning models. It delves into techniques such as image augmentation, normalization, and feature extraction, which are essential for preparing cancer images for analysis.

Next, the book delves into the realm of cancer susceptibility and recurrence prediction, employing deep learning algorithms to analyze large datasets and identify patterns that can predict the likelihood of cancer development. It discusses various machine learning models, including convolutional neural networks, recurrent neural networks, and decision trees, and their applications in predicting cancer risk and outcomes.

Furthermore, the book explores the detection of different cancers using deep learning techniques. It discusses the use of convolutional neural networks for image classification, where trained models can identify cancerous cells with high accuracy. It also covers the use of transfer learning and fine-tuning approaches to enhance the performance of deep learning models on specific cancer types.

The book also addresses the complexity and challenges associated with deep learning-based cancer diagnostics. It discusses the limitations of current datasets, the need for more annotated data, and the ethical considerations surrounding the use of personal medical information in machine learning algorithms.

In conclusion, "Deep Learning Approaches in Cancer Diagnostics" is a seminal work that revolutionizes the field of cancer diagnostics by leveraging the power of deep learning. It provides a comprehensive and up-to-date overview of the state-of-the-art approaches, covering preprocessing data, cancer susceptibility and recurrence prediction, detection of different cancers, and complexity and challenges. This book is a valuable resource for researchers, practitioners, and healthcare professionals interested in advancing the field of cancer diagnostics and improving patient outcomes.

Weight: 450g
Dimension: 241 x 160 x 17 (mm)
ISBN-13: 9781032233857