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Khalid Shaikh Sabitha Krishnan Rohit Thanki Artificial Intelligence in Breast Cancer Early Detection and Diagnosis Artificial Intelligence in Breast Cancer Early Detection and Diagnosis Khalid Shaikh • Sabitha Krishnan • Rohit Thanki Artificial Intelligence in Breast Cancer Early Detection and Diagnosis Khalid Shaikh Sabitha Krishnan Prognica Labs Prognica Labs Dubai, United Arab Emirates Dubai, United Arab Emirates Rohit Thanki Prognica Labs Dubai, United Arab Emirates ISBN 978-3-030-59207-3 ISBN 978-3-030-59208-0 (eBook) https://doi.org/10.1007/978-3-030-59208-0 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Preface Cancer is a disease that propagates due to the uncontrollable growth of abnormal cells. Abnormal cells perform unusual functions rather than a predefined function within the human body. The growth of normal cells is controllable, while the growth pattern of abnormal cells is recalcitrant. Within a momentary period, the numbers of abnormal cells in the body increase, which in turn creates further complications for proper bodily functions. Cancerous cells can target any organ in a functional body; organs such as skin, lung, and breast are commonly affected. Breasts contain mam- mary glands and are recognized not only as an important organ but also as a paired structure, which is located on the pectoral region of the chest wall of the human body. They are present in both males and females yet are more prominent in females following puberty. The accessory gland of the female reproductive system’s primary function is to produce milk. Breast cancer is a condition in which cells in the breast grow out of control. Various types of breast cancer can originate, depending on which cells are adversely affected. Our book aims to provide definitive information regarding cancer and breast cancer. The risk factors and prevention of breast cancer are carefully articulated in this book. Information on multiple machine learning and deep learning-based algo- rithms, along with its working and, usage are thoughtfully discussed in this book. The role of artificial intelligence in the healthcare arena, including medical imaging, is up for debate. Computer aided detection (CADe) system and computer aided diagnosis (CADx) systems along with detection and diagnosis of breast cancer are intricately described. Finally, deep learning AI models, including their performance within the classi- fication of breast cancer, are introduced and detailed in our effort. We sincerely hope that we can contribute in the eventual technological evolution within the early detection of cancer. v vi Preface Overview of the Book In Chap. 1, basic information on cancer and breast cancer are discussed. The various stages and types of breast cancer are beautifully discussed in the Chap. 2. The infor- mation on artificial intelligence and various learning algorithms are covered in Chap. 3. In this chapter, the working of various learning algorithms’ advantages and limitations are discussed. The role of artificial intelligence and learning algorithms in healthcare sector and medical imaging are covered in the Chap. 4. The informa- tion of various stages of computer aided system for detection and diagnosis of breast cancer are covered in the Chap. 5. Finally, Chap. 6 gives a deep learning model along with its performance for the classification of breast cancer. Features of the Book • Basic information on cancer and breast cancer • Various stages and types of breast cancer • Details of learning algorithms and their role in healthcare and medical imaging analysis • Computer aided system for detection and diagnosis of breast cancer • Inclusion of deep learning model for detection and diagnosis of breast cancer Dubai, UAE Khalid Shaikh Rohit Thanki Sabitha Krishnan Acknowledgments Our task has been easier, and the final version of the book considerably better because of the help we have received. I would also like to thank the team at Springer, in particular, Michael McCabe, senior editor, Springer, for their helpful guidance and encouragement during the creation of this book. Dubai, UAE Khalid Shaikh Rohit Thanki Sabitha Krishnan vii Contents 1 An Introduction to Breast Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Introduction to Cancer and Its Treatment . . . . . . . . . . . . . . . . . . . . 1 1.1.1 History of Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.1.2 Oldest Theories on Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.1.3 Risk Factors for Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.4 Classification of Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.1.5 Types of Cancer Based on the Affected Organ . . . . . . . . . . 7 1.1.6 Cancer Diagnosis Methods . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.1.7 Cancer Treatment Methods . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.2 Breast Anatomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.3 What Is Breast Cancer? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.4 Risk Factors for Breast Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.4.1 Common Risk Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.4.2 Lifestyle-Related Risk Factors . . . . . . . . . . . . . . . . . . . . . . . 16 1.4.3 Controversial Risk Factors . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.4.4 Unclear Risk Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 1.5 Prevention Against Breast Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . 18 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2 Types, Diagnosis, and Treatment of Breast Cancer . . . . . . . . . . . . . . . 21 2.1 Stages of Breast Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.1.1 Stage 0 and Stage 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.1.2 Stage 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.1.3 Stage 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.1.4 Stage 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.2 Types of Breast Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.2.1 Ductal Carcinoma in Situ (DCIS) . . . . . . . . . . . . . . . . . . . . 23 2.2.2 Invasive Ductal Carcinoma (IDC) . . . . . . . . . . . . . . . . . . . . 24 2.2.3 Triple-Negative Breast Cancer . . . . . . . . . . . . . . . . . . . . . . . 24 2.2.4 Inflammatory Breast Cancer (IBC) . . . . . . . . . . . . . . . . . . . 24 2.2.5 Metastatic Breast Cancer (MBC) . . . . . . . . . . . . . . . . . . . . . 24 ix x Contents 2.2.6 Other Types of Breast Cancer . . . . . . . . . . . . . . . . . . . . . . . 25 2.3 Early Detection of Breast Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.3.1 Basic Symptoms and Signs of Breast Cancer . . . . . . . . . . . 26 2.3.2 Breast Cyst . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.3.3 Breast Self-Exam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.4 Diagnosis of Breast Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.4.1 Diagnostic Mammogram . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.4.2 Ultrasound . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.4.3 MRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.4.4 Biopsy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.5 Treatment for Breast Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.5.1 Surgery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.5.2 Chemotherapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 2.5.3 Radiation Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 2.5.4 Hormone Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.5.5 Targeted Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3 Artificial Intelligence and Learning Algorithms . . . . . . . . . . . . . . . . . 37 3.1 Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.2 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.2.1 Supervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.2.2 Unsupervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.2.3 Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.3 Supervised Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.3.1 Statistical Learning-Based Algorithms . . . . . . . . . . . . . . . . 40 3.3.2 Nearest Neighbors (NN) Algorithm . . . . . . . . . . . . . . . . . . . 41 3.3.3 Naïve Bayes (NB) Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 42 3.3.4 Support Vector Machine (SVM) . . . . . . . . . . . . . . . . . . . . . 43 3.3.5 Discriminant Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.3.6 Decision Tree (DT) Algorithm . . . . . . . . . . . . . . . . . . . . . . . 46 3.3.7 Random Forest (RF) Algorithm . . . . . . . . . . . . . . . . . . . . . . 47 3.3.8 Linear Regression (LR) Algorithm . . . . . . . . . . . . . . . . . . . 47 3.3.9 Logistic Regression Algorithm . . . . . . . . . . . . . . . . . . . . . . 48 3.3.10 Ensemble Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.4 Unsupervised Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.4.1 K-Means Clustering Algorithm . . . . . . . . . . . . . . . . . . . . . . 50 3.4.2 Principal Component Analysis . . . . . . . . . . . . . . . . . . . . . . . 51 3.4.3 Independent Component Analysis . . . . . . . . . . . . . . . . . . . . 52 3.4.4 Singular Value Decomposition . . . . . . . . . . . . . . . . . . . . . . . 52 3.4.5 Gaussian Mixture Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.4.6 Self-Organizing Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.4.7 Hidden Markov Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Contents xi 3.5 Reinforcement Learning Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.5.1 Basics of RL Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.5.2 Q-Learning Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3.5.3 State Action Reward State Action (SARSA) Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3.5.4 Deep Q Network (DQN) Algorithm . . . . . . . . . . . . . . . . . . 57 3.6 Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 3.6.1 Convolutional Neural Network (CNN) Algorithm . . . . . . . 58 3.6.2 Other Deep Learning Algorithms. . . . . . . . . . . . . . . . . . . . . 61 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4 AI in Healthcare and Medical Imaging. . . . . . . . . . . . . . . . . . . . . . . . . 67 4.1 AI in Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 4.1.1 AI-Based Healthcare Research . . . . . . . . . . . . . . . . . . . . . . 68 4.1.2 Application of AI in Healthcare . . . . . . . . . . . . . . . . . . . . . . 69 4.1.3 Industries Work in Healthcare Area . . . . . . . . . . . . . . . . . . . 71 4.2 AI in Medical Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 4.2.1 Various Types of Medical Imaging Techniques . . . . . . . . . . 73 4.2.2 Computer-Aided Detection (CADe) and Computer-Aided Diagnosis (CADx) . . . . . . . . . . . . . . . 74 4.2.3 Application of AI in Medical Imaging . . . . . . . . . . . . . . . . . 76 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 5 Breast Cancer Detection and Diagnosis Using AI . . . . . . . . . . . . . . . . 79 5.1 Various Imaging Techniques for the Detection of Breast Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.1.1 Breast Mammography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.1.2 Breast Ultrasound. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 5.1.3 Breast MRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 5.1.4 Newest Imaging Techniques for Detection of Breast Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 5.2 Various Stages for Detection and Diagnosis of Breast Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.2.1 Breast Image Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 5.2.2 Breast Image Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . 88 5.2.3 Breast Image Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . 88 5.2.4 Feature Extraction of Breast Tissue . . . . . . . . . . . . . . . . . . . 89 5.2.5 Classification of Breast Cancer . . . . . . . . . . . . . . . . . . . . . . 89 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 6 Deep Learning Model for Classification of Breast Cancer . . . . . . . . . 93 6.1 Various Breast Cancer Imaging Datasets . . . . . . . . . . . . . . . . . . . . . 93 6.1.1 Mammographic Image Analysis Society (MIAS) Digital Mammogram Dataset . . . . . . . . . . . . . . . . . . . . . . . . 93

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