Artificial Intelligence in Cancer Diagnosis and Prognosis, Volume 2

This book covers state-of-the-art artificial intelligence techniques used to diagnose
breast and bladder cancer, focusing on non-invasive approaches. Cancer is the
leading cause of death worldwide, regardless of the type of malignancy. As recently
as 2020, about ten million people died due to cancer worldwide. The early detection
of cancer tremendously increases the patient’s chances of survival. Unfortunately,
most cancer patients are diagnosed in the final stages of the disease. Recently,
artificial intelligence and deep learning have shown great ability to address this issue.
This volume of the book will focus on the use of artificial intelligence techniques for
the early diagnosis of breast and bladder Cancer. After skin cancer, breast cancer is
the most diagnosed type of cancer in women in the United States. Women and men
can both get breast cancer, but it is more common in women than in men. Bladder
cancer is usually detected in its early stages, but it is common for it to come back
after treatment, which requires frequent checkups. Among the topics discussed in the
book are the development of artificial neural networks for breast histopathology
image analysis; machine learning in bladder cancer diagnosis; deep learning in
photoacoustic breast cancer imaging; histopathological breast cancer image classification; machine learning and biofliuid metabolomics for breast cancer diagnosis;
and machine learning analysis of breast cancer single-cell omics data.
In summary, the main aim of this book is to help advance scientific research
within the broad field of the early detection of breast and bladder cancer. The book
focuses on major trends and challenges in this area, and it presents work that aims to
identify new techniques and describe their use in biomedical analysis.

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