Machine Learning and Deep Learning Techniques for Medical Science

K. Gayathri Devi, Kishore Balasubramanian, Le Anh Ngoc

• Publisher: CRC Press
• Year: 2022
• Print ISBN: 9781032104201
• eBook ISBN: 9781000583366
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Computer Science General
• Index: Technology & Engineering

The application of machine learning is growing exponentially into every branch of business and science, including medical science. This book presents the integration of machine learning (ML) and deep learning (DL) algorithms that can be applied in the healthcare sector to reduce the time required by doctors, radiologists, and other medical professionals for analyzing, predicting, and diagnosing the conditions with accurate results. The book offers important key aspects in the development and implementation of ML and DL approaches toward developing prediction tools and models and improving medical diagnosis.

The contributors explore the recent trends, innovations, challenges, and solutions, as well as case studies of the applications of ML and DL in intelligent system-based disease diagnosis. The chapters also highlight the basics and the need for applying mathematical aspects with reference to the development of new medical models. Authors also explore ML and DL in relation to artificial intelligence (AI) prediction tools, the discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, and pattern recognition approaches to functional magnetic resonance imaging images.

This book is for students and researchers of computer science and engineering, electronics and communication engineering, and information technology; for biomedical engineering researchers, academicians, and educators; and for students and professionals in other areas of the healthcare sector.

• Presents key aspects in the development and the implementation of ML and DL approaches toward developing prediction tools, models, and improving medical diagnosis
• Discusses the recent trends, innovations, challenges, solutions, and applications of intelligent system-based disease diagnosis
• Examines DL theories, models, and tools to enhance health information systems
• Explores ML and DL in relation to AI prediction tools, discovery of drugs, neuroscience, and diagnosis in multiple imaging modalities

Dr. K. Gayathri Devi is a Professor at the Department of Electronics and Communication Engineering, Dr. N.G.P Institute of Technology, Tamil Nadu, India.

Dr. Kishore Balasubramanian is an Assistant Professor (Senior Scale) at the Department of EEE at Dr. Mahalingam College of Engineering & Technology, Tamil Nadu, India.

Dr. Le Anh Ngoc is a Director of Swinburne Innovation Space and Professor in Swinburne University of Technology (Vietnam).

1 A Comprehensive Study on MLP and CNN, and the Implementation of Multi-Class Image Classification using Deep CNN

S.P. Balamurugan

Assistant Professor/Programmer, Department of Computer and Information Science, Faculty of Science, Annamalai University, Tamil Nadu, India

DOI: 10.1201/9781003217497-1

CONTENTS

1. 1.1 Introduction
2. 1.2 The Processes of the Neural Network
3. 1.2.1 Basics of Neural Network
4. 1.2.1.1 Architecture of Neural Network
5. 1.2.1.2 Working Principles of Neural Network
6. 1.2.1.3 Learning Methods of Neural Network
7. 1.2.1.4 Drawbacks of Neural Network
8. 1.2.2 Convolutional Neural Network (CNN) Algorithm
9. 1.2.2.1 Merits of CNN over MLP
10. 1.2.2.2 Contents of CNN
11. 1.2.2.3 Working of CNN Algorithm
12. 1.2.2.4 Deep CNN
13. 1.3 Experimental Procedure
14. 1.3.1 Preparing the Dataset
15. 1.3.2 Model Training and Testing
16. 1.4 Results and Discussion
17. 1.4.1 MNIST Dataset Image Classifications
18. 1.4.2 CIFAR-10 Dataset Image Classifications
19. 1.5 Conclusion
20. References

1.1 Introduction

Its use in various fields has increased as the efficiency of computer-assisted image processing has improved. Basic image processing techniques include restoration, enhancement, segmentation, and classification, etc. Image classification is critical in image processing. The goal of image classification is to assign images to the same class category automatically [1]. The classification may be carried out in two ways such as supervised and unsupervised. The two processes involved in image classification are training and testing. During training, the visual features are retrieved and combined to generate a unique description for each class. Depending on the type of classification challenge, such as binary or multi-class classification, the preceding method is repeated for all classes. The test images are presented to the trained model to categorize the class during testing. This assigning of classes is done based on the training features. Deep learning, also known as hierarchical learning has been a hot topic in machine learning research since 2006. Deep learning is a class of machine learning algorithms that use multiple layers of non-linear information processing for supervised or unsupervised feature extraction, transformation, pattern analysis, and classification, according to a standard definition [2]. One of the most widely used types of ANN approaches is the MLP approach. It is a member of the ANN's FFNN class structure. An FFNN framework of MLP comprises neuron that is gathered in layers. In the MLP method, the entire input nodes in input and hidden layers are dispersed to several hidden layers [3,4]. The CNN is applied in image prediction and classification, and a minimal rate of error is accomplished that is less than the maximum human error rate [5,6]. Additionally in [6], CNN is utilized for training the maximum number of images and classifying the tomato leaf's diseases. It is one of the interventions in CNN which has accomplished maximum prediction and classification. The DCNN model will convolve more of the input data. It extracts more relevant features and achieves better accuracy for bigger datasets than CNN [7,8]. In [7], DCNN is employed for classifying the interstitial lu...

Table of contents

• Cover
• Half Title
• Series Page
• Title Page
• Copyright Page
• Contents
• Editor Biographies
• List of Contributors
• Chapter 1 A Comprehensive Study on MLP and CNN, and the Implementation of Multi-Class Image Classification using Deep CNN
• Chapter 2 An Efficient Technique for Image Compression and Quality Retrieval in Diagnosis of Brain Tumour Hyper Spectral Image
• Chapter 3 Classification of Breast Thermograms using a Multi-layer Perceptron with Back Propagation Learning
• Chapter 4 Neural Networks for Medical Image Computing
• Chapter 5 Recent Trends in Bio-Medical Waste, Challenges and Opportunities
• Chapter 6 Teager-Kaiser Boost Clustered Segmentation of Retinal Fundus Images for Glaucoma Detection
• Chapter 7 IoT-Based Deep Neural Network Approach for Heart Rate and SpO2 Prediction
• Chapter 8 An Intelligent System for Diagnosis and Prediction of Breast Cancer Malignant Features using Machine Learning Algorithms
• Chapter 9 Medical Image Classification with Artificial and Deep Convolutional Neural Networks: A Comparative Study
• Chapter 10 Convolutional Neural Network for Classification of Skin Cancer Images
• Chapter 11 Application of Artificial Intelligence in Medical Imaging
• Chapter 12 Machine Learning Algorithms Used in Medical Field with a Case Study
• Chapter 13 Dual Customized U-Net-based Based Automated Diagnosis of Glaucoma
• Chapter 14 MuSCF-Net: Multi-scale, Multi-Channel Feature Network using Resnet-Based Attention Mechanism for Breast Histopathological Image Classification
• Chapter 15 Artificial Intelligence is Revolutionizing Cancer Research
• Chapter 16 Deep Learning to Diagnose Diseases and Security in 5G Healthcare Informatics
• Chapter 17 New Approaches in Machine-based Image Analysis for Medical Oncology
• Chapter 18 Performance Analysis of Deep Convolutional Neural Networks for Diagnosing COVID-19: Data to Deployment
• Chapter 19 Stacked Auto Encoder Deep Neural Network with Principal Components Analysis for Identification of Chronic Kidney Disease
• Index