Advances in Deep Learning for Medical Image Analysis

Archana Mire, Vinayak Elangovan, Shailaja Patil

• Publisher: CRC Press
• Year: 2022
• Print ISBN: 9781032137162
• eBook ISBN: 9781000575996
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Computer Science General
• Index: Technology & Engineering

This reference text introduces the classical probabilistic model, deep learning, and big data techniques for improving medical imaging and detecting various diseases.

The text addresses a wide variety of application areas in medical imaging where deep learning techniques provide solutions with lesser human intervention and reduced time. It comprehensively covers important machine learning for signal analysis, deep learning techniques for cancer detection, diabetic cases, skin image analysis, Alzheimer's disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. The text will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering.

1ANFIS-Based Cardiac Arrhythmia Classification

Alka Barhatte

MIT World Peace University, Pune, India

Manisha Dale

MES’s College of Engineering, Pune, India

Rajesh Ghongade

Bharati Vidyapeeth College of Engineering, Pune, India

DOI: 10.1201/9781003230540-1

1.1Introduction

The electrocardiogram (ECG) is a cardiac signal representing the recording of the electrical activity of the heart. Information such as heart rate, rhythm, and morphology in the form of conduction disturbances can be extracted from the ECG signal. The significance of the ECG is notable in that coronary heart diseases are major causes of mortality worldwide. The ECG varies between different individuals, due to the anatomy of the heart, and differences in size, position, age, etc. Thus, the ECG yields highly distinctive characteristics, suitable for various applications and diagnosis. This chapter focuses on cardiac arrhythmia classification. Cardiac arrhythmia is a heart disorder displaying an irregular heartbeat due to malfunction in the cells of the heart’s electrical system. During cardiac arrhythmia, the heartbeat can have an irregular rhythm. Sometimes it is too fast – >90 beats/min – and this is called tachycardia; when the heartbeat is too slow – <60 beats/min – this is called bradycardia. Thus, there are many types of cardiac arrhythmia based on heart rate and site of origin. Some are frequently benign, although several may be a sign of significant heart disease, stroke, or surprising heart failure. At some stage in cardiac arrhythmia, the heart may not be capable of pumping enough blood to the body. Lack of blood flow can damage organs like the brain and heart. Thus, to enable appropriate survival measures, an accurate classification is required of cardiac arrhythmia that leads to heart rate variations. This chapter introduces the classification of six types of cardiac arrhythmias based on the adaptive neuro-fuzzy inference system (ANFIS).

This chapter is structured as follows. Section 1.2 gives a review of the literature. Section 1.3 describes system design and QRS complex detection and features the extraction method used in the classifier system. Section 1.4 describes system implementation using proposed methodology. Section 1.5 presents results and analysis and finally section 1.6 gives a discussion and conclusion.

1.2 Review of the Literature

Despite the ease of obtaining data, challenges remain for us to extract reliable information from biomedical signals. This can be a very demanding task for a computerized automated system for ...

Table of contents

• Cover
• Half-Title Page
• Title Page
• Copyright Page
• Dedication
• Contents
• Preface
• Editor Biographies
• List of Contributors
• 1 ANFIS-Based Cardiac Arrhythmia Classification
• 2 Two-Stage Deep Learning Architecture for Chest X-Ray-Based COVID-19 Prediction
• 3 White Blood Cell Classification Using Conventional and Deep Learning Techniques: A Comparative Study
• 4 Comparison and Performance Evaluation Using Convolution Neural Network-Based Deep Learning Models for Skin Cancer Image Classification
• 5 A Review of Breast Cancer Detection Using Deep Learning Techniques
• 6 Artificial Intelligence and Machine Learning: A Smart Science Approach for Cancer Control
• 7 Detection of Diabetic Foot Ulcer Using Machine/Deep Learning
• 8 Review of Deep Learning Techniques for Prognosis and Monitoring of Diabetes Mellitus
• Index