El-Baz A., Suri J.S. (eds.) Artificial Intelligence in Cancer Diagnosis and Prognosis. Volume 1: Lung and kidney cancer

IOP Publishing Ltd, 2022. — 251 p. — ISBN 978-0-7503-3593-5.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 classi fication; machine learning and biofliuid metabolomics for breast cancer diagnosis; and machine learning analysis of breast cancer single-cell omics data.Preface
American Joint Committee on Cancer staging of lung and renal cancers using a recurrent deep neural network model
Neural-ensemble-based detection: a modern way to diagnose lung cancer
Computed tomography and magnetic resonance imaging machine learning applications for renal cell carcinoma
Pulmonary nodule-based feature learning for automated lung tumor grading using convolutional neural networks
Detection of lung contours using closed principal curves and machine learning
Bytes, pixels, and bases: machine learning in imaging–omics for renal cell carcinoma
Detection, growth quantification, and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scans
Training a deep multiview model using small samples of medical data
Overview of deep learning for lung cancer diagnosis
Artificial intelligence for cancer diagnosis
Lung cancer diagnosis using 3D-CNN and spherical harmonics expansions