Recent Advances in AI-enabled Automated Medical Diagnosis PDF
Preview Recent Advances in AI-enabled Automated Medical Diagnosis
Recent Advances in AI-enabled Automated Medical Diagnosis Editors Richard Jiang Lancaster University, UK Li Zhang Royal Holloway University of London, UK Hua-Liang Wei University of Sheffield, UK Danny Crookes Queen’s University Belfast, UK Paul Chazot Durham University, UK p, A SCIENCE PUBLISHERS BOOK First edition published 2022 by CRC Press 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742 and by CRC Press 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN © 2022 Richard Jiang, Li Zhang, Hua-Liang Wei, Danny Crookes and Paul Chazot CRC Press is an imprint of Taylor & Francis Group, LLC Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, access www.copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978- 750-8400. For works that are not available on CCC please contact [email protected] Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging-in-Publication Data (applied for) ISBN: 978-1-032-00843-1 (hbk) ISBN: 978-1-032-00856-1 (pbk) ISBN: 978-1-003-17612-1 (ebk) DOI: 10.1201/9781003176121 Typeset in Times New Roman by Innovative Processors Preface Recent years have witnessed significant advancement of machine learning (ML), artificial intelligence (AI) and other data-driven techniques, bringing medical and healthcare data analysis to a new era with exciting progress in many areas including biomedical informatics, health informatics, AI in medicine, AI of Medical Things (AIoMT), healthcare AI and smart homecare. It is an amazing age where opportunities and challenges exist equally in nearly all application areas for all these modern machine learning and data analytics techniques. We are lucky because we have now achieved many state-of-the-art results in medical diagnosis and healthcare services using different advanced machine learning techniques. For example, nowadays many shallow and deep learning frameworks and AI techniques have been successfully applied to medical diagnosis and healthcare services. However, there are still many challenges that need to be overcome to obtain satisfactory solutions when trying to resolve complicated problems. For example, there are some increasing concerns about data-driven AI and black-box deep neural networks from the viewpoints of reliability, ethics, explainability and interpretability, among others. We have seen an increasing amount of applications of machine learning, artificial intelligence and other data analytics (e.g. data modelling, data mining) techniques in the fields of medical diagnosis and healthcare services, such as neurophysiological signal and neuro-imaging processing, cancer and disease diagnosis, healthcare (e.g. electronic health records) and wellbeing data analysis, pandemic diagnosis and prediction, just mention a few. A lot of new methods and algorithms have been developed, aiming to solve either the existing problems or emerging issues which are closely related to or significantly impact on our daily life. It is right time to stimulate the dissemination of recent results and findings of applying AI and ML techniques to solving problems in the fields of medical diagnosis and healthcare services. This book aims to show the most recent advancement of data-driven and data-based ML and AI techniques, with an emphasis on the implementation of methods and algorithms, including signal processing and system identification, data mining, image processing and pattern recognition, and deep neural networks, together with their applications. The book collects a total of 22 papers, which can be roughly categorized into 4 groups: 1) COVID-19 diagnosis and pandemic prediction (Chapters 1, 2, 3, 4, 5 and 15); 2) Neurophysiological and neurobiological data analysis and brain–computer interface (Chapters 17, 18, 19, 20, 21 and 22); 3) Biological and healthcare data analysis (Chapters 13, 14, 15 and 16); and 4) Disease and cancer diagnosis (Chapters 7, 8, 9, 10, 11 and 12). Some details are as follows. iv Preface The emergency of COVID pandemic has engrossed vast efforts across worldwide research communities. Chapter 1 developed AI-based COVID-19 diagnosis based on texture analysis. Chapter 2 proposed a novel transparent, interpretable, parsimonious, and simulatable (TIPS) modelling framework to reveal the COVID-19 pandemic dynamics. Chapter 3 combined audio with CT scan for COVID-19 diagnosis. Chapter 4 reported the COVID-19 forecasting in India with locally collected data. Chapter 5 carried out a survey on COVID diagnosis techniques. Medical AI has also been widely exploited in many specific cases. Chapter 6 exploited AI-based age estimation models in the evaluation of human brains. Chapter 7 carried out a survey on the brain age estimation. Chapter 8 reviewed the social behavioural evaluation on animals. Chapter 9 reports a new AI model on Leukaemia Diagnosis. Chapter 10 investigated on AI-powered medical prediction on trauma patients. Chapter 11 exploited AI techniques for face-based emotion evaluation. Chapter 12 utilized blood vessel segmentation for retinal related eye analysis. Time-series medical sensory data and signal can always play an important role in medical diagnosis. Chapter 13 analysed the dynamics of the walking gaits of Parkinson’s patients. Chapter 14 examined neural dynamics via sparse model identification. Chapter 15 studied the correlation between weather conditions and the spread of COVID pandemic via contrastive learning with NARMAX models. Chapter 16 exploited NARX predictive model on body mass index. Brain waves and heart signals are two particularly widely exploited time-series data in various applications. Chapter 17 utilized brain-computer interfaces in the therapy for motor rehabilitation. Chapter 18 proposed new time-series modelling ECG based heart anomaly detection. Chapter 19 carried out a comparative study on several deep neural networks for ECG based cardiologic diagnosis. Chapter 20 compared convolutional neural networks and long-sort term memory neural networks in EEG based emotion diagnosis. Chapter 21 proposed a novel approach on motor imagery EEG classification. Chapter 22 studied the EEG signal classification for brain-computer interface. It is expected that scientists, academics, researchers, clinicians, medical services, policy makers, technologists, engineers and so on who work in the areas of medicine and healthcare, and those who are interested in some of the subjects in these areas, would be able to benefit from the book. Richard Jiang Li. Zhang Hua-Liang Wei Contents Preface iii 1. Enhancement of COVID-19 Diagnosis Using Machine Learning and Texture Analyses of Lung Imaging 1 Bhuvan Mittal, JungHwan Oh 2. Modeling COVID-19 Pandemic Dynamics Using Transparent, Interpretable, Parsimonious and Simulatable (TIPS) Machine Learning Models: A Case Study from Systems Thinking and System Identification Perspectives 13 Hua-Liang Wei, Stephen A. Billings 3. Deep Learning-based Respiratory Anomaly and COVID Diagnosis Using Audio and CT Scan Imagery 29 Conor Wall, Chengyu Liu and Li Zhang 4. COVID-19 Forecasting in India through Deep Learning Models 41 Arindam Chaudhuri and Soumya K. Ghosh 5. Deep Learning-based Techniques in Medical Imaging for COVID-19 Diagnosis: A Survey 62 Fozia Mehboob, Abdul Rauf, Khalid M. Malik, Richard Jiang, Abdul K.J. Saudagar, Abdullah AlTameem and Mohammed AlKhathami 6. Embedding Explainable Artificial Intelligence in Clinical Decision Support Systems: The Brain Age Prediction Case Study 81 Angela Lombardi, Domenico Diacono, Nicola Amoroso, Alfonso Monaco, Sabina Tangaro and Roberto Bellotti 7. Machine Learning-based Biological Ageing Estimation Technologies: A Survey 96 Zhaonian Zhang, Richard Jiang, Danny Crookes and Paul Chazot 8. Review on Social Behavior Analysis of Laboratory Animals: From Methodologies to Applications 110 Ziping Jiang, Paul L. Chazot and Richard Jiang 9. Acute Lymphoblastic Leukemia Diagnosis Using Genetic Algorithm and Enhanced Clustering-based Feature Selection 123 Siew Chin Neoh, Srisukkham Worawut, Li Zhang and Md. Mostafa Kamal Sarker vi Contents 10. Artificial Intelligence-enabled Automated Medical Prediction and Diagnosis in Trauma Patients 135 Lianyong Li, Changqing Zhong, Gang Wang, Wei Wu, Yuzhu Guo, Zheng Zhang, Bo Yang, Xiaotong Lou, Ke Li and Heming Yang 11. DCGAN-based Facial Expression Synthesis for Emotion Well-being Monitoring with Feature Extraction and Cluster Grouping 146 Eaby Kollonoor Babu, Kamlesh Mistry and Li Zhang 12. A Hybrid-DE for Automatic Retinal Image-based Blood Vessel Segmentation 157 Colin Paul Joy, Kamlesh Mistry, Gobind Pillai and Li Zhang 13. Artificial Intelligence for Accurate Detection and Analysis of Freezing of Gait in Parkinson’s Disease 173 Wenting Yang, Simeng Li, Debin Huang, Hantao Li, Lipeng Wang, Wei Zhang and Yuzhu Guo 14. Sparse Model Identification for Nonstationary and Nonlinear Neural Dynamics Based on Multiwavelet Basis Expansion 215 Song Xu, Lina Wang and Jingjing Liu 15. How Weather Conditions Affect the Spread of COVID-19: Findings of a Study Using Contrastive Learning and NARMAX Models 238 Yiming Sun and Hua-Liang Wei 16. New Measurement of the Body Mass Index with Bioimpedance Using a Novel Interpretable Takagi-Sugeno Fuzzy NARX Predictive Model 253 Changjiang He, Yuanlin Gu, Hua-Liang Wei and Qinggang Meng 17. Training Therapy with BCI-based Neurofeedback Systems for Motor Rehabilitation 268 Jingjing Luo, Qiying Chen, Hongbo Wang, Youhao Wang and Qiang Du 18. A Modified Dynamic Time Warping (MDTW) and Innovative Average Non-self Match Distance (ANSD) Method for Anomaly Detection in ECG Recordings 281 Xinxin Yao and Hua-Liang Wei 19. An Investigation on ECG-based Cardiological Diagnosis via Deep Learning Models 304 Alex Meehan, Zhaonian Zhang, Bryan Williams and Richard Jiang 20. EEG-based Deep Emotional Diagnosis: A Comparative Study 317 Geyi Liu, Zhaonian Zhang, Richard Jiang, Danny Crookes and Paul Chazot 21. A Novel Motor Imagery EEG Classification Approach Based on Time- Frequency Analysis and Convolutional Neural Network 329 Qinghua Wang, Lina Wang and Song Xu 22. Classification of EEG Signals for Brain-Computer Interfaces using a Bayesian-Fuzzy Extreme Learning Machine 347 Adrian Rubio-Solis, Carlos Beltran-Perez and Hua-Liang Wei Index 363 CHAPTER 1 Enhancement of COVID-19 Diagnosis Using Machine Learning and Texture Analyses of Lung Imaging Bhuvan Mittal1, JungHwan Oh2 Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, U.S.A. 1. Introduction The COVID-19 is a highly contagious and virulent disease caused by the Severe Acute Respiratory Syndrome – Coronavirus-2 (SARS-CoV-2). Over 186 million cases and 4.0 million deaths were reported worldwide as of 13 July, 2021 [17]. A multinational consensus from the Fleischner Society reported that Computerized Tomography (CT) can be utilized for an early diagnosis of CT-based COVID-19 [14]. CT also has a high sensitivity in the classification of the COVID-19 disease [8]. However, this binary classification task of identifying COVID-19 positive from negative patients from lung CT involves a significant amount of radiologists’ time and effort. Thus, it is crucial to develop an automated analysis of CT images to save radiologists’ time in overstretched healthcare environments. In our previous work [12], we implemented the CoviNet, a 3D CNN-based model for COVID-19 classification of lung CT images. In this paper, we propose an enhancement of this model [12], CoviNet Enhanced, using 3D CNN and Leung- Malik (LM) texture features [15] additionally. CoviNet Enhanced has a novel conditional majority voting algorithm with an ensemble of 3D CNN and SVMs, which provide better classification sensitivity and specificity than using the 3D CNN alone [12]. Since the SVM algorithm is complementary, this hybrid approach using SVM models and 3D CNN combined via conditional majority voting shows superior performance. Contributions to CoviNet Enhanced: To achieve a better classification sensitivity and specificity than those of CoviNet, we add: 1) Leung-Malik (LM) texture features [15], 2) an ensemble classifier comprising a 3D CNN and texture features-based SVMs, and 3) the final classification is done via conditional majority voting. The remainder of this paper is organized as follows: related work is presented in Section 2; the proposed method is described in Section 3; in Section 4, we discuss our experimental setup and results; finally, Section 5 presents some concluding remarks. 2 Recent Advances in AI-enabled Automated Medical Diagnosis 2. Related Work In our previous work [12], we classified the CT into COVID-19 positive or negative, based on the 3D CNN which comprises 3D filters in convolutional layers to train a deep 3D CNN from scratch. This model utilized the 3D CT scan volumes as opposed to individual slice-level CT imaging data to come up with the patient-level diagnosis. It has a network depth of 16 layers comprising four 3D convolutional layers, four 3D max-pooling layers, four 3D batch normalization layers, one global average 3D pooling layer, two fully connected dense layers, one dropout layer, and a final softmax layer. All four convolutional layers have a kernel size of 3×3×3 but use different numbers of kernels at 64, 128, and 256. The ‘ReLU’ activation function is used. The four 3D-max-pooling layers take a 2×2×2 sliding cube which subsamples the image length, width, and depth dimensions and has a stride of 2. To speed up the training of CoviNet, 3D batch normalization layers are included after the 3D pooling layers. Then, 3D global average pooling takes a 4D input of size length×width×depth×channels (=12×12×2×256) and outputs a one-dimensional output of size 256 channels. Next, the fully connected layer follows with a dimension of 512, followed by a dropout layer with a dropout factor of 0.3 which is introduced to make the model robust to noise. The final softmax layer with sigmoid activation outputs the predicted probability of being COVID-19 positive. Adam optimizer is used with an initial learning rate of 0.001 with an exponential decay rate of 0.96 over 100,000 decay steps. In Wang et al.’s DeCovNet [16], a single 3D CNN classifier, taking only original CTs as input data, is implemented for COVID-19 classification into positive and negative classes. In DeCovNet, the 3D convolutional layers are followed by 3D batch normalization, ‘ReLU’ activation, and 3D pooling layers. There are six 3D convolutional layers. Training and testing are done on a proprietary dataset for 100 epochs having an Adam optimizer with a constant learning rate of 1×10-5. Imani et al. [11] first used morphological filters to extract the shape and structural features without and with CNN processing. Second, they used Gabor filters to extract textural features without and with the use of a trained CNN for feature extraction. Then they classified the morphological and Gabor features independently via two separate classifiers: support vector machine and random forest. For the morphological model, the random forest classifier without CNN performed the best, whereas, for the Gabor model, the random forest classifier with trained CNN performed the best. Goncharov et al. [4] proposed a multitask approach to solving the identification and segmentation tasks together via a 2D U-Net model which outputs a common intermediate feature map that is aggregated into a feature vector via a pyramid pooling layer. The classification layers follow the high-resolution upper part of U-Net and comprise two fully-connected layers, followed by a softmax layer which outputs the probability of the input CT volume being COVID-19 positive. 2.1 Methodology The proposed approach consists of four components: Enhancement of COVID-19 Diagnosis Using Machine Learning... 3 2.1.1 3D CNN A 3D CNN, having four three-dimensional convolutional kernel layers from our previous work [12], is trained from scratch on the augmented CT data. The augmentation details can be found in our previous work [12], and the 3D CNN architecture was explained in detail in Section 2. It is a deep model with 17 layers comprising four 3D convolutional layers, four 3D max-pooling layers, four 3D batch normalization layers, one global average 3D pooling layer, two fully connected dense layers, one dropout layer, and a final softmax layer. 3D batch normalization layers after the 3D pooling layers help the model learn faster, and the dropout layer contributes to its noise robustness. Early stopping is also invoked if the model loss does not reduce over the last five epochs. 2.1.2 Texture Feature Extraction The LM features have textural, shape, and intensity-based features. LM texture features [15] are typically extracted for an image using the 48 LM filters (LM filter bank) which are convolved over the entire input image. The LM filter bank (Fig. 1) has a mix of edge, bar, and spot filters at multiple scales and orientations. It has a total of 48 filters, namely two Gaussian derivative filters at six orientations and three scales, eight Laplacian of Gaussian filters, and four Gaussian filters. Fig. 1: LM filter bank with 48 filters [15] When we apply the two Gaussian derivative filters at six orientations and three scales shown in the first three rows of Fig. 1, we have nearly all-black pixels as a result, since lungs do not have elongated objects. The features which can identify the ground glass opacities and consolidations will be suitable for our work. The results of convolving the 12 filters in the bottom row of Fig. 1 are shown in Fig. 2. Intuitively, these are possibly informative features, which can be used for COVID-19 classification. We create 12 separate SVM models, taking one feature at a time for all the convolved outputs from the 12 filters. Then, the final feature selection is done by selecting the two top-performing LM filters. The selected top-performing filters are LM37 and LM41 and the results of convolving these filters with the input image are shown in Fig. 3. 2.1.3 SVM Models The selected LM texture features, namely the LM37 and LM41, along with the
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