1st Edition

Medical Data Analysis and Processing using Explainable Artificial Intelligence

Edited By Om Prakash Jena, Mrutyunjaya Panda, Utku Kose Copyright 2024
    268 Pages 105 B/W Illustrations
    by CRC Press

    The text presents concepts of explainable artificial intelligence (XAI) in solving real world biomedical and healthcare problems. It will serve as an ideal reference text for graduate students and academic researchers in diverse fields of engineering including electrical, electronics and communication, computer, and biomedical

    • Presents explainable artificial intelligence (XAI) based machine analytics and deep learning in medical science
    • Discusses explainable artificial intelligence (XA)I with the Internet of Medical Things (IoMT) for healthcare applications
    • Covers algorithms, tools, and frameworks for explainable artificial intelligence on medical data
    • Explores the concepts of natural language processing and explainable artificial intelligence (XAI) on medical data processing
    • Discusses machine learning and deep learning scalability models in healthcare systems

    This text focuses on data driven analysis and processing of advanced methods and techniques with the help of explainable artificial intelligence (XAI) algorithms. It covers machine learning, Internet of Things (IoT), and deep learning algorithms based on XAI techniques for medical data analysis and processing. The text will present different dimensions of XAI based computational intelligence applications. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and biomedical engineering.

    Chapter 1 Explainable AI (XAI): Concepts and Theory

    Tanvir Habib Sardar, Sunanda Das, Bishwajeet Kumar Pandey


      1. Introduction
      2. Formal Definitions of Explainable Artificial Intelligence
      3. The Working Mechanism of Explainable Artificial Intelligence: How Explainable Artificial Intelligence Generates Explanations
      4. How Humans Reason (with Errors)
      5. How Explainable Artificial Intelligence Support Reason and Solve Human Error Issue
      6. Applications and Impact Areas of Explainable Artificial Intelligence
        1. Threat Detection
        2. Object Detection
        3. Adversarial ML Prevention
        4. Open Source Intelligence (OSI)
        5. Automated Medical Diagnosis
        6. Autonomous Vehicles

      7. Benefits of Explainable Artificial Intelligence
      8. Research Challenges of Explainable Artificial Intelligence
      9. Use Cases of Explainable Artificial Intelligence
      10. Limitations of Explainable Artificial Intelligence
      11. Conclusion


    Chapter 2: Utilizing Explainable Artificial Intelligence to Address Deep Learning in Biomedical Domain

    Priyanka Sharma


    2.1 Introduction: Background and Driving Forces

    2.2 XAI Taxonomy

    2.3 Review of State of Art

    2.3.1 Methods focused on features

    2.3.2 Global methods

    2.3.3 Concept Methods

    2.3.4 Surrogate Methods

    2.3.5 Local, Pixel-based Techniques

    2.3.6 Human Centered Methods

    2.4 Deep Learning –Reshaping Healthcare

    2.4.1 Deep Learning Methods Multi-layer Perceptron or Deep Feed Forward Neural Network Restricted Boltzmann Machine Deep Belief Network Autoencoder Convolutional Neural Network Recurrent Neural Network Long Short- Term Memory (LSTM) and Gated Recurrent Unit (GRU)

    2.4.2 Deep Learning Applications in Healthcare

    2.5 Results

    2.6 Benefits and Drawbacks of XAI Methods

    2.7 Conclusion

    Chapter 3 Explainable Fuzzy Decision Tree for Medical Data Classification

    Authors: Swathi Jamjala Narayanan, Boominathan Perumal, Sangeetha Saman


    3.1. Introduction

    3.2. Literature survey

    3.3. Fuzzy classification problem

    3.4. Induction of fuzzy decision tree

    3.4.1 Fuzzy c-means clustering (FCM)

    3.4.2 Cluster validity indices and Optimality Condition Separation and Compactness (SC) Compact Overlap (CO) Fukuyama and Sugeno (FS) Xie and Beni (XB) Partition entropy Fuzzy hyper volume (FHV) PBMF Partition coefficient

    3.4.3 Basics of developing Fuzzy ID3

    3.5. Case Study: Explainable FDT for HCV Medical Data

    3.6. Conclusion and Future work

    Chapter 4 Statistical Algorithm for Change Point Detection in Multivariate Time Series of Medicine Data Based on Principles of Explainable Artificial Intelligence

    D. Klyushin, A. Urazovskyi


    4.1 Introduction

    4.2 Detection of change points in multivariate time series

    4.3 Petuninʼs ellipses and ellipsoids

    4.4 Numerical experiments

    4.4.1 Almost non-overlapped uniform distributions with different locations

    4.4.2 Uniform distributions with different locations that initially
    are strongly overlapped, then slightly overlapped,
    and finally are not overlapped

    4.4.3 Almost non-overlapped normal distributions with different locations

    4.4.4 Normal distributions with the same location and scales that are gradually
    begin to differ

    4.4.5 Normal distributions with the same locations and strongly different scales

    4.4.6. Exponential distributions with different parameters

    4.4.7 Gamma-distributions with the same location and different scales

    4.4.8 Gamma-distributions with different locations and the same scale

    4.4.9 Gumbel distributions with different locations and the same scale

    4.4.10 Gumbel distributions with the same location and different scales

    4.4.11 Rayleigh distributions with different scales

    4.4.12 Laplace distributions with different means and the same variance

    4.4.13 Laplace distributions with the same location and different scales

    4.4.14 Logistic distributions with different locations and the same scale

    4.4.15 Logistic distributions with the same location and different scales

    4.4.16 Conclusion on numerical experiments

    4.5 Quasi-real experiments

    4.5.1 Simulation of tachycardia

    4.5.2 Simulation of coronavirus pneumonia

    4.5.3 Simulation of cancer lung

    4.5.4 Simulation of physical activity

    4.5.5 Simulation of stress/panic attack

    4.5.6 Conclusion on quasi-real experiments

    4.6 Conclusion

    4.7 References

    Chapter 5 XAI and Machine learning for Cyber security: A Systematic Review

    Gousia Habib*, Shaima Qureshi

    5.1. Introduction to Explainable AI (XAI).

    5.2 Principles followed by XAI Algorithm.

    5.3 Types of Explainability.

    5.4 Some Critical Applications of Explainability


    5.5 Related Work.

    5.6 Historical Origins of the Need for Explainable AI.

    5.7 Taxonomy of MAP of explainability approaches.

    5.8 Challenges posed by XAI.

    5.8.1 A Black Box Attack on XAI in Cybersecurity.

    5.8.2 Manipulation of Adversarial Models to Deceive Neural Network Interpretations.

    5.8.3 Geometry is responsible for the manipulation of explanations.

    5.8.4 Saliency Method's Unreliability.

    5.8.5 Misleading Black Box Explanations are used to manipulate user trust.


    5.9 Various Suggested Solutions for XAI security Challenges

    5.9.1 Addressing Manipulation of User Trust through Misleading Black Box Explanations.

    5.9.2 Improved Interpretability of Deep Learning.

    5.9.3 Heat-map explanations Defense against adversarial cyber-attacks.

    5.9.4 Curvature minimization.

    5.9.5 Weight decay.

    5.9.6 Smoothing activation functions.

    5.10 Conclusion


    Chapter 6 Classification and regression tree (CART) modelling approach to predict the number of lymph node dissection among endometrial cancer patients

    Prafulla Kumar Swain, Manas Ranjan Tripathy, Pravat Kumar Sarangi, Smruti Sudha Pattnaik


    6.1 Introduction

    6.2 Data source

    6.3 Methods used

    6.3.1 Regression Tree

    6.3.2 Optimal threshold value (cut off point)

    6.3.3 Regression tree algorithm

    6.3.4 Optimal threshold value (cut off point)

    6.3.5 Validation of models

    6.4 Applications to EC Data

    6.5 Discussion

    6.6 Conclusion


    Chapter 7: Automated Brain Tumor Analysis using Deep Learning based Framework

    Amiya Halder, Rudrajit Choudhuri, Apurba Sarkar

    7.1 Introduction

    7.2 Related Works

    7.3 Background

    7.3.1 Autoencoder

    7.3.2 Convolutional Autoencoder

    7.3.3 Pre-trained Deep Classification Architectures

    7.4 Proposed Methodology

    7.4.1 Image Denoising

    7.4.2 Tumor Detection and Tumor Grade Classification Data Acquisition Experimental Setup: Fine Tuning the Architectures

    7.4.3 Model Training

    7.5 Result Analysis

    7.5.1 Evaluation Metrics

    7.5.2 Performance Evaluation

    7.6 Conclusion

    Chapter 8 A Robust Framework for Prediction of Diabetes Mellitus using Machine Learning

    Sarthak Singh, Rohan Singh, Arkaprovo Ghosal, Tanmaya Mahapatra


    8.1 introduction

    8.2 Background

    8.3 Related Work

    8.4 Conceptual Approach

    8.5 Evaluation

    8.6 Discussion

    8.7 Conclusion


    Chapter 9 Effective Feature Extraction for Early Recognition and Classification of Triple Modality Breast Cancer Images Using Logistic Regression Algorithm

    Manjula Devarakonda Venkata , Sumalatha Lingamgunta


    9.1 Introduction

    9.2 Symptoms of Breast Cancer

    9.3 Need for Early detection

    9.4 Datasets used

    9.5 Classification of Medical features from three modalities using Logistic

    Regression Algorithm

    9.5.1 Pre processing

    9.6 Results

    9.6.1 Pre-processed US images

    9.6.2 Preprocessed Mammogram Image

    9.6.3 Pre processed MRI images

    9.7 Conclusion


    Chapter 10: Machine Learning and Deep Learning Models Used to Detect Diabetic Retinopathy and Its Stages

    S. Karthika, M. Durgadevi


    1. Introduction

    1. Conventional ML & DL Algorithms

    1. ML - Support Vector Machine (ML- SVM)
    2. ML - K_Nearest Neighbors (ML- KNN)
    3. ML - Random Forest (ML- RF)
    4. ML - Neural Networks (ML- NN)
    5. Deep Learning (DL)
    6. DL - Classic Neural Networks

    1. DL - Convolutional Neural Networks (DL- CNN)
    2. DL - LSTMNs (Long Short-Term Memory Networks)
    3. DL - Recurrent Neural Networks (DL- RNN)
    4. DL - Generative Adversarial Networks (DL - GAN)
    5. DL -Reinforcement Learning

    1. Retinal Image Datasets used in DR detection

    1. DR Process Detection

    1. Non-Proliferative Diabetic Retinopathy
    2. Proliferative diabetic retinopathy

      1. Techniques for Detecting Microaneurysms (MA)
      2. Techniques for Detecting Hemorrhage (HEM)
      3. Techniques for Detecting Exudate (EX)
      4. Techniques for Detecting Macular Edema

    1. Diabetic Retinopathy Lesion Segmentation
    2. Performance Metrics

    1. True Positive
    2. True Negative
    3. False Positive
    4. False Negative

    1. Conclusion


    Chapter 11 Clinical Natural Language Processing Systems for Information Retrieval from Unstructured Medical Narratives

    S. Lourdumarie Sophie, S. Siva Sathya

    11.1 Introduction

    11.2 Components of NLP

    11.2.1 Natural Language Understanding (NLU)

    11.2.2 Natural Language Generation (NLG)

    11.3 Stages of NLP

    11.3.1 Phonological Analysis

    11.3.2 Morphological and Lexical Analysis

    11.3.3 Syntactic Analysis

    11.3.4 Semantic Analysis

    11.3.5 Discourse Integration

    11.3.6 Pragmatic Analysis

    11.4 Applications & Techniques

    11.4.1 Optical Character Recognition (OCR)

    11.4.2 Named Entity Recognition (NER)

    11.4.3 Question Answering

    11.4.4 Chatbots

    11.4.5 Machine Translation

    11.4.6 Sentiment Analysis

    11.4.7 Topic Modelling

    11.4.8 Automatic Text Summarization (ATS)

    11.4.9 Co-reference Resolution

    11.4.10 Disease Prediction

    11.4.11 Text Classification

    11.4.12 Cognitive Assistant (CA)

    11.4.13 Automatic Speech Recognition (ASR)

    11.5 NLP Systems in Health Care

    11.6 Conclusion



    Dr. Om Prakash Jena is currently working as an Assistant Professor in the Department of Computer Science, Ravenshaw University, Cuttack, and Odisha, India. He has 11 years of teaching and research experience in the undergraduate and post-graduate levels. He has published several technical papers in international journals/conferences/edited book chapters of reputed publications. He is a member of IEEE, IETA, IAAC, IRED, IAENG, and WACAMLDS. His current research interest includes Database, Pattern Recognition, Cryptography, Network Security, Artificial Intelligence, Machine Learning, Soft Computing, Natural Language Processing, Data Science, Compiler Design, Data Analytics, and Machine Automation. He has many edited books, published by Wiley, CRC press, Taylor & Francis Bentham Publication into his credit and also the author of four textbooks under Kalyani Publisher. He also serve as a reviewer committee member and editor of many international journals.

    Dr. Mrutyunjaya Panda holds a Ph.D degree in Computer Science from Berhampur University. He obtained his Master in Engineering from Sambalpur University, MBA in HRM from IGNOU, New Delhi, and Bachelor in Engineering from Utkal University in 2002, 2009, 1997 respectively. He is having more than 20 years of teaching and research experience. He is presently working as Reader in P.G. Department of Computer Science and Applications, Utkal University, Bhubaneswar, Odisha, India. He is a member of MIR Labs (USA), KES (Australia), IAENG ( Hong Kong), ACEEE(I), IETE(I), CSI(I), ISTE(I). He has published about 70 papers in International and national journals and conferences. He has also published 7 book chapters to his credit. He has 2 text books and 3 edited books to his credit. He is a program committee member of various international conferences. He is acting as a reviewer of various international journals and conferences of repute. He is an Associate Editor of IJCINI Journal, IGI Global, USA and an Editorial board member of IJKESDP Journal of Inderscience, UK. He is also a Special issue Editor of International Journal of Computational Intelligence Studies (IJCIStudies), Inderscience, UK. His active area of research includes Data Mining, Image processing, Intrusion detection and prevention. Social networking, Mobile Communication, wireless sensor networks, Natural language processing, Internet of Things, Text Mining etc.

    Dr. Utku Kose received the B.S. degree in 2008 from computer education of Gazi University, Turkey as a faculty valedictorian. He received M.S. degree in 2010 from Afyon Kocatepe University, Turkey in the field of computer and D.S. / Ph. D. degree in 2017 from Selcuk University, Turkey in the field of computer engineering. Between 2009 and 2011, he has worked as a Research Assistant in Afyon Kocatepe University. Following, he has also worked as a Lecturer and Vocational School - Vice Director in Afyon Kocatepe University between 2011 and 2012, as a Lecturer and Research Center Director in Usak University between 2012 and 2017, and as an Assistant Professor in Suleyman Demirel University between 2017 and 2019. Currently, he is an Associate Professor in Suleyman Demirel University, Turkey. He has more than 100 publications including articles, authored and edited books, proceedings, and reports. He is also in editorial boards of many scientific journals and serves as one of the editors of the Biomedical and Robotics Healthcare book series by CRC Press. His research interest includes artificial intelligence, machine ethics, artificial intelligence safety, optimization, the chaos theory, distance education, e-learning, computer education, and computer science.