Handbook of Deep Learning in Biomedical Engineering: Techniques and Applications

By Valentina Emilia Balas and Raghvendra Kumar

Deep Learning (DL) is a method of machine learning, running over Artificial Neural Networks, that uses multiple layers to extract high-level features from large amounts of raw data. Deep Learning methods apply levels of learning to transform input data into more abstract and composite information. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications gives readers a complete overview of the essential concepts of Deep Learning and its applications in the field of Biomedical Engineering. Deep learning has been rapidly developed in recent years, in terms of both methodological constructs and practical applications. Deep Learning provides computational models of multiple processing layers to learn and represent data with higher levels of abstraction. It is able to implicitly capture intricate structures of large-scale data and is ideally suited to many of the hardware architectures that are currently available. The ever-expanding amount of data that can be gathered through biomedical and clinical information sensing devices necessitates the development of machine learning and AI techniques such as Deep Learning and Convolutional Neural Networks to process and evaluate the data. Some examples of biomedical and clinical sensing devices that use Deep Learning include: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), Magnetic Particle Imaging, EE/MEG, Optical Microscopy and Tomography, Photoacoustic Tomography, Electron Tomography, and Atomic Force Microscopy. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications provides the most complete coverage of Deep Learning applications in biomedical engineering available, including detailed real-world applications in areas such as computational neuroscience, neuroimaging, data fusion, medical image processing, neurological disorder diagnosis for diseases such as Alzheimer’s, ADHD, and ASD, tumor prediction, as well as translational multimodal imaging analysis.

• Presents a comprehensive handbook of the biomedical engineering applications of DL, including computational neuroscience, neuroimaging, time series data such as MRI, functional MRI, CT, EEG, MEG, and data fusion of biomedical imaging data from disparate sources, such as X-Ray/CT
• Helps readers understand key concepts in DL applications for biomedical engineering and health care, including manifold learning, classification, clustering, and regression in neuroimaging data analysis
• Provides readers with key DL development techniques such as creation of algorithms and application of DL through artificial neural networks and convolutional neural networks
• Includes coverage of key application areas of DL such as early diagnosis of specific diseases such as Alzheimer’s, ADHD, and ASD, and tumor prediction through MRI and translational multimodality imaging and biomedical applications such as detection, diagnostic analysis, quantitative measurements, and image guidance of ultrasonography

• Language: English
• Publisher: Academic Press
• Release date: Nov 12, 2020
• ISBN: 9780128230473

Book preview

Handbook of Deep Learning in Biomedical Engineering

Techniques and Applications

Editors

Valentina Emilia Balas

Full Professor, Department of Automatics and Applied Software Aurel Vlaicu University of Arad, Romania

Brojo Kishore Mishra

Professor, Department of CSE, School of Engineering GIET University, India

Raghvendra Kumar

Associate Professor, Department of Computer Science and Engineering, School of Engineering GIET University, India

Table of Contents

Cover image

Title page

Copyright

Contributors

About the editors

Preface

Key features

About the book

1. Congruence of deep learning in biomedical engineering: future prospects and challenges

1. Introduction

2. Fire module

3. Background study

4. Study of various types of model

5. Proposed method by the authors

6. Conclusion and future work

2. Deep convolutional neural network in medical image processing

1. Introduction

2. Medical image analysis

3. Convolutional neural network and its architectures

4. Application of deep convolutional neural network in medical image analysis

5. Critical discussion: inferences for future work and limitations

6. Conclusion

3. Application, algorithm, tools directly related to deep learning

1. Introduction

2. Tools used in deep learning

3. Algorithms

4. Applications of deep learning

5. Conclusion

4. A critical review on using blockchain technology in education domain

1. Introduction

2. Consortium blockchain and its suitability for e-governance

3. Consensus

4. Attacks on blockchain

5. Blockchain in education domain

6. Scalability challenges

7. Security challenges

8. Conclusion

5. Depression discovery in cancer communities using deep learning

1. Introduction

2. Related work

3. Proposed system architecture

4. Models

5. Conclusion

6. Plant leaf disease classification based on feature selection and deep neural network

1. Introduction

2. Literature review

3. Our proposed framework

4. Results

5. Conclusion

7. Early detection and diagnosis using deep learning

1. Introduction

2. Diagnostics using deep learning

3. Early detection of diseases using deep learning

4. Conclusion and further advancements

8. A review on plant diseases recognition through deep learning

1. Introduction

2. Plant diseases

3. Traditional methods to treat plant diseases

4. Innovative detection method

5. Remote sensing of plant diseases

6. Plant disease detection by well-known deep learning architectures

7. Conclusions

9. Applications of deep learning in biomedical engineering

1. Introduction

2. Biomedical engineering

3. Deep learning

4. Most popular deep neural networks architectures used in biomedical applications

5. Convolutional neural network

6. Convolution layer

7. Pooling layer

8. Fully convolutional layer

9. Applications of convolutional neural network in biomedicine

10. Recurrent neural network

11. Applications of recurrent neural network in biomedicine

12. Generative adversarial networks

13. Applications of generative adversarial network in biomedicine

14. Deep belief network

15. Pretraining stage

16. Fine-tuning stage

17. Applications of deep learning in biomedicine

18. Biomedical image analysis

19. Image detection and recognition

20. Image acquisition and image interpretation

21. Image segmentation

22. Cytopathology and histopathology

23. Brain, body, and machine interface

24. Classification of the brain–machine interfaces

25. Invasive techniques

26. Noninvasive techniques

27. Body–machine interface

28. Drug infusion system

29. Rehabilitation system

30. Diseases diagnosis

31. Omics

32. Around the genome

33. Protein-binding prediction

34. DNA–RNA-binding proteins

35. Gene expression

36. Alternative splicing

37. Gene expression prediction

38. Genomic sequencing

39. Around the protein

40. Protein Structure Prediction

41. Protein secondary structure prediction

42. Protein Interaction Prediction

43. Public and medical health management

44. Conclusion

10. Deep neural network in medical image processing

1. Literature review

2. Digital image and computer vision

3. Deep learning

4. Segmentation techniques in image processing

5. Conclusion

Index

Copyright

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Contributors

Rashmi Agrawal,     Manav Rachna International Institute of Research and Studies (MRIIRS), Faridabad, Haryana, India

B. Balamurugan,     School of Computing Science and Technology, Galgotias University, Greater Noida, Uttar Pradesh

Aradhana Behura,     Veer Surendra Sai University of Technology, Burla, Sambalpur, Odisha, India

Saakshi Bhargava,     Department of Physical Sciences and Engineering, Banasthali Vidyapith, Tonk, Rajasthan, India

Son Dao,     International University, Vietnam National University - Ho Chi Minh City, Ho Chi Minh City, Vietnam

Jayashankar Das,     Centre for Genomics and Biomedical Informatics, Institute of Medical Sciences and SUM Hospital, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha, India

R. Indrakumari,     School of Computing Science and Technology, Galgotias University, Greater Noida, Uttar Pradesh

Aashna Jha,     Department of Electronics and Communication, Netaji Subhas University of Technology, New Delhi, India

Vaishali Kalra,     The NorthCap University, Gurugram, Haryana, India

Supriya Khaitan,     School of Computing Science and Technology, Galgotias University, Greater Noida, Uttar Pradesh

M. Nagoor Meeral,     PG Research Department of Computer Science, Sadakathullah Appa College, Tirunelveli, Tamil Nadu, India

Subhashree Mohapatra,     Department of Computer Science and Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha, India

S. Shajun Nisha,     PG Research Department of Computer Science, Sadakathullah Appa College, Tirunelveli, Tamil Nadu, India

Tan Pham,     International University, Vietnam National University - Ho Chi Minh City, Ho Chi Minh City, Vietnam

T. Poongodi,     School of Computing Science and Technology, Galgotias University, Greater Noida, Uttar Pradesh

Shrddha Sagar,     School of Computing Science and Technology, Galgotias University, Greater Noida, Uttar Pradesh

Deepak Kumar Sharma,     Department of Information Technology, Netaji Subhas University of Technology, New Delhi, India

Srishti Sharma,     The NorthCap University, Gurugram, Haryana, India

Pawan Singh,     Department of Information Technology, Netaji Subhas University of Technology, New Delhi, India

G. Sudha Sadasiuvam,     Department of CSE, PSG College of Technology, Coimbatore, Tamil Nadu, India

M. Mohamed Sathik,     PG Research Department of Computer Science, Sadakathullah Appa College, Tirunelveli, Tamil Nadu, India

Tripti Swarnkar,     Department of Computer Application, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha, India

Siddharth Verma,     Manav Rachna International Institute of Research and Studies (MRIIRS), Manav Rachna Campus, Faridabad, Haryana, India

About the editors

Valentina Emilia Balas, Brojo Kishore Mishra, and Raghvendra Kumar

Valentina E. Balas is currently Full Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, Aurel Vlaicu University of Arad, Romania. She holds a Ph.D. Cum Laude, in Applied Electronics and Telecommunications from Polytechnic University of Timisoara. Dr. Balas is author of more than 350 research papers in refereed journals and International Conferences. Her research interests are in Intelligent Systems, Fuzzy Control, Soft Computing, Smart Sensors, Information Fusion, Modeling and Simulation. She is the Editor-in Chief to International Journal of Advanced Intelligence Paradigms (IJAIP) and to International Journal of Computational Systems Engineering (IJCSysE), member in Editorial Board member of several national and international journals and is evaluator expert for national, international projects and PhD Thesis. Dr. Balas is the director of Intelligent Systems Research Centre in Aurel Vlaicu University of Arad and Director of the Department of International Relations, Programs and Projects in the same university. She served as General Chair of the International Workshop Soft Computing and Applications (SOFA) in nine editions organized in the interval 2005-2020 and held in Romania and Hungary. Dr. Balas participated in many international conferences as Organizer, Honorary Chair, Session Chair, member in Steering, Advisory or International Program Committees and Keynote Speaker. Now she is working in a national project with EU funding support: BioCell-NanoART = Novel Bio-inspired Cellular Nano-Architectures - For Digital Integrated Circuits, 3M Euro from National Authority for Scientific Research and Innovation. She is a member of European Society for Fuzzy Logic and Technology (EUSFLAT), member of Society for Industrial and Applied Mathematics (SIAM) and a Senior Member IEEE, member in Technical Committee – Fuzzy Systems (IEEE Computational Intelligence Society), chair of the Task Force 14 in Technical Committee – Emergent Technologies (IEEE CIS), member in Technical Committee – Soft Computing (IEEE SMCS). Dr. Balas was past Vice-president (responsible with Awards) of IFSA - International Fuzzy Systems Association Council (2013-2015), is a Joint Secretary of the Governing Council of Forum for Interdisciplinary Mathematics (FIM), - A Multidisciplinary Academic Body, India and recipient of the Tudor Tanasescu Prize from the Romanian Academy for contributions in the field of soft computing methods (2019).

Dr. Raghvendra Kumar is working as an Associate Professor in Computer Science and Engineering Department at GIET University, India. He received his BTech, MTech, and PhD in Computer Science and Engineering, India, and Postdoc Fellow from Institute of Information Technology, Virtual Reality and Multimedia, Vietnam. He serves as a Series Editor of Internet of Everything: Security and Privacy Paradigm, Green Engineering and Technology: Concepts and Applications, published by CRC press and Taylor & Francis Group, USA, and Bio-Medical Engineering: Techniques and Applications, published by Apple Academic Press, CRC Press, and Taylor & Francis Group, USA. He also serves as an acquisition editor for Computer Science by Apple Academic Press, CRC Press, and Taylor & Francis Group, USA. He has published a number of research papers in international journal (SCI/SCIE/ESCI/Scopus) and conferences, including IEEE and Springer. He has also served as the organizing chair (RICE 2019, 2020), volume editor (RICE 2018), keynote speaker, session chair, cochair, publicity chair, publication chair, advisory board, technical program committee member in many international and national conferences, and guest editor in many special issues from reputed journals (indexed by Scopus, ESCI, SCI). He also published 13 chapters in edited book published by IGI Global, Springer, and Elsevier. His researches areas are computer networks, data mining, cloud computing and secure multiparty computations, theory of computer science, and design of algorithms. He has authored and edited 23 computer science books in field of Internet of things, data mining, biomedical engineering, big data, and robotics published by IGI Global Publication, USA, IOS Press, Netherlands, Springer, Elsevier, and CRC Press, USA.

Dr. Brojo Kishore Mishra is currently working as a Professor in the Department of Computer Science and Engineering at the GIET University, Gunupur-765022, India. He received his PhD degree in Computer Science from the Berhampur University in 2012. He has published more than 30 research papers in national and international conference proceedings, 25 research papers in peer-reviewed journals, and 22 book chapters; authored 2 books; and edited 4 books. His research interests include data mining, machine learning, soft computing, and security. He has organized and co-organized local and international conferences and also edited several special issues for journals. He is the Senior Member of IEEE and Life Member of CSI, ISTE. He is the Editor of CSI Journal of Computing.

Preface

Deep learning has been rapidly developed in the recent years, in terms of both methodological development and practical applications. It provides computational models of multiple processing layers to learn and represent data with multiple levels of abstraction. It is able to implicitly capture intricate structures of large-scale data and is ideally suited to some of the hardware architectures that are currently available. The purpose of this book is to provide a diverse, but complementary, set of contributions to demonstrate new developments and applications of deep learning and computational machine learning to solve problems in biomedical engineering. The proposed book will be organized as a reference source for enabling readers to have an idea about the relation between deep learning and biomedical engineering.

In Chapter 1, survey of deep learning is used for image classification, carotid ultrasound data investigation, cardiotocography, intravascular ultrasound report, lung CT report, brain tumor prediction, object detection, segmentation, breast cancer prediction, ECG (electrocardiogram) signal, EEG (electroencephalogram), PPG signal registration, and psoriasis skin disease as well as cancer detection. Concise summaries are delivered of trainings per application zone: pulmonary, musculoskeletal neuro, digital pathology, and abdominal, retinal, breast, and cardiac. There are various types of deep learning techniques present to improve accuracy of the medical dataset.

Chapter 2 provides a detailed discussion on different convolutional neural network (CNN) architectures and their applications in the medical imaging domain. Moreover, a state-of-the-art comparison has been carried out between several existing works inside medical imaging based on CNN. Lastly, the work concludes with several critical remarks highlighting future challenges and their solutions.

Chapter 3 discusses about the class of tools that enable deep learning engineers to actually do their work faster and more effectively. Some of the tools include TensorFlow, Keras, Caffe, and Torch. Deep learning models make use of several kinds of advanced algorithms. Some algorithms are best suited to perform specific tasks. In order to choose the right ones, it is good to gain grasp of all primary algorithms. Excellent knowledge of advanced deep learning techniques, their types, and applications can help users execute them for various purposes.

Chapter 4 aims at critically analyzing the existing techniques used in blockchains and their suitability in education domain. The advantages and challenges in using blockchain-based applications in education are also discussed in this chapter. Security breaches and attacks on using blockchains are discussed along with possible countermeasures. A plan of how existing models can be improved to enhance performance of blockchains in applications belonging to education is also discussed.

Chapter 5 investigates the use of different deep neural network architectures and natural language processing for depression detection in cancer communities. Depression detection using sentiment affect can be of great assistance to the doctors treating cancer patients and aid them in deciding whether along with the cancer treatment their patients need help from psychologists or psychiatrists.

Chapter 6 focuses on early disease recognition that requires high-resolution images. After a preprocessing step using a contrast enhancement method, all the diseased blobs are segmented for the whole dataset. A list of several measurement-based features that represent the blobs is chosen and selected based on principle component analysis. The features are used as inputs for a standard feedforward neural network. Our results show competitive classification results not only with other deep learning approaches, such as CNNs, but also with a simpler network structure.

Chapter 7 determines how deep learning helps in the early diagnosis of several diseases such as Alzheimer's disease, rheumatic diseases, autism spectrum disorder, and more. After expanding upon the basics of deep learning and biomedical engineering, the chapter explores more upon diagnostics using deep learning and discusses the early diagnosis of certain diseases.

Chapter 8 details on the advancement in the subset of machine learning; the deep learning made this research area into high potential in terms of precise prediction and accuracy. Many versions of deep learning–based architecture are implemented along with various nonvisualization and visualization techniques to classify and detect the symptoms of plant disease with several performance metrics. This chapter illustrates a comprehensive review of deep learning models used to detect plant diseases, and in some cases, the diseases have been identified before the symptoms appear clearly.

Chapter 9 discusses about fundamentals of biomedical engineering and deep learning. It also explores about applications of deep learning in various problems of biomedical field.

Chapter 10 discusses the fundamentals of image processing first, including segmentation and edge detection, followed by identifying critical areas in biomedical images, denoising, and applying image processing technique on various available biomedical image datasets. The authors focus on tissue segmentation, application of CNN in interpreting biomedical images, usage of different deep learning libraries for identifying areas of interest in a biomedical image, computer-aided disease diagnosis or prognosis, and so on. We will conclude by raising research issues and suggesting future directions for further improvements.

The aim of this book is to support the computational studies at the research and postgraduation level with open problem-solving techniques. We are confident that it will bridge the gap for them by supporting novel solution in their problem solving. At the end, editors have taken utmost care while finalizing the chapters to the book, but we are open to receive your constructive feedback, which will enable us to carry out necessary points in our forthcoming books.

Valentina Emilia Balas

Brojo Kishore Mishra

Raghvendra Kumar

Key features

1. Covers the evolution of deep learning in biomedical engineering and healthcare from fundamental theories to present forms

2. Presents diversified medical applications of deep learning with use cases

3. Includes contributors from different parts of the world

4. Explores deep learning and machine learning techniques along with biomedical engineering applications

5. Presents from multiple perspectives such as academics, industry, and research fields

6. Emphasizes on the advancements and cutting-edge technologies throughout

7. Focuses on different tools, platforms, and techniques

About the book

Deep learning (DL) is a method of machine learning, as running over artificial neural networks, which has a structure above the standards to deal with large amounts of data. This is generally because of the increasing amount of data, input data sizes, and, of course, greater complexity of objective real-world problems. Research studies performed in the associated literature show that the DL currently has a good performance among considered problems and seems to be a strong solution for more advanced problems of the future. In this context, this book aims to provide some essential information about DL and its applications within the field of biomedical engineering. Due to numerous biomedical information sensing devices, such as computed tomography, magnetic resonance imaging, ultrasound, single photon emission computed tomography, positron emission tomography, magnetic particle imaging, EE/MEG, optical microscopy and tomography, photoacoustic tomography, electron tomography, and atomic force microscopy, large amount of biomedical information was gathered these years. This poses a great challenge on how to develop new advanced imaging methods and computational models for efficient data processing, analysis, and modeling in clinical applications and in understanding the underlying biological process.

1: Congruence of deep learning in biomedical engineering

future prospects and challenges

Aradhana Behura     Veer Surendra Sai University of Technology, Burla, Sambalpur, Odisha, India

Abstract

Deep learning models have opened up many prospects in medical images for achieving unprecedented performance, for example, classification of tissues and division or segmentation are a few medical outcomes. This chapter evaluates and describes the convolutional neural network (CNN) intended for characterization of tissue in clinical imaging, which is applied for segregating essential metastatic liver tumors from diffusion-weighted magnetic resonance imaging information. Advancement in the field of deep learning for normal pictures has provoked a surge of enthusiasm for applying comparative strategies to clinical images. Most of the initial attempts replaced the input of a deep CNN with medical images, which does not consider the basic contrasts between these two kinds of pictures. In particular, fine details are fundamental in clinical pictures, unlike regular images where coarse structures are very important. This distinction makes it difficult to utilize the current organized models created for common pictures, because they chip away at downscaled medical images to decrease the memory prerequisites. These subtleties are important to provide accurate detection. Furthermore, a medical test in clinical imaging regularly accompanies many perspectives, which must be intertwined to arrive at the right conclusion. A survey of deep learning is used for image classification, carotid ultrasound data investigations, cardiotocography, intravascular ultrasound reports, lung computed tomography reports, brain tumor prediction, coronavirus prediction (COVID-19) object detection, segmentation, breast cancer prediction, electrocardiogram signals, electroencephalograms, photoplethysmographic signal registration, psoriasis skin disease, as well as cancer detection. Concise summaries are delivered of trainings per application zone: pulmonary, musculoskeletal neuro, digital pathology, abdominal, retinal, breast, and cardiac. There are various types of deep learning techniques present to improve the accuracy of the medical dataset. Deep reinforcement learning, recursive neural network, multilayer perceptron, recurrent neural network, Boltzmann machine, and CNN are different types of deep learning techniques used to train the image and signal dataset. Generative adversarial network (GAN), autoencoder, and deep belief neural network are subcategories of unsupervised pretrained neural network. Some well-known architectural models of CNNs are ResNet (2015), VGGNet (2014), SqueezeNet (2016), GoogLeNet (2014), and ZFNet (2013) and are the visualization concept of the deconvolutional network; AlexNet (2012) and LeNet (Peng et al., 2009; Mitchell; Bengio, 2012; Dutkowski et al., 2015; Han et al., 2020 [16–20]) are basically used to train image datasets; the long short-term memory technique is used to train signalized datasets; and RHSBoost and genetically optimized neural network are used for efficient multiple classification of datasets. Dimensionality reduction, feature extraction, overfitting, underfitting, and normalization problems can be solved using various types of optimization algorithm. Image security is another important part, and by using an autoencoder, GAN network, and CNN we can prevent alteration in the medical image. Minor alteration of the medical image is very dangerous to patient life. By using deep learning and steganography, we can first compress as well as train the dataset, then security can be preserved after embedding of watermarks (which is a secret image visible to the human eye that cannot be altered; this steganography concept is called watermarking).

Keywords

Biomedical image and signal processing; Breast cancer; Deep learning; Image segmentation; Unsupervised feature learning

1. Introduction

Death from breast cancer [7,8,11,14,34,35] may be avoided by detecting the risks of medical patients and discussing them effectively [3]. One of the known risks for tumor development besides age, gender, gene mutations, and family history is the comparative sum of radiodense tissue in the female breast, called mammographic density [26–30]. By using a stacked autoencoder we can more accurately predict brain tumors. C-means, K-means, and DBSCAN clustering techniques are used to detect affected areas in medical images. There are various types of nature-inspired algorithms used to optimize the performance of clustering that provide better results. Segmentation of brain [31] and liver tumors provides [9,10,12,13,23] important biomarkers for medical diagnosis [24,25]. Here, we present and authenticate a procedure to integrate an improved edge pointer and derive an initial curve for magnetic resonance imaging (MRI)-based disease segmentation from the dataset. At the preprocessing step, the computed tomography (CT) image intensity values were truncated to lie in a fixed range to enhance the image contrast surrounding the organ and the disease-affected area. To eliminate nonliver tissues for the following segmentation of the disease, the liver is segmented by two convolutional neural networks (CNNs) [15,21–23] in a coarse-to-fine manner. Here, we present a new procedure for combining high-resolution photorealistic medical images from the semantic label charts with conditional generative adversarial networks (GANs). A GAN [32,33] is a type of deep learning method made up of two neural networks in conflict with each other in a zero-sum game outline.

The combination of two neural networks that make up an architecture of a GAN are:

• a generator with the objective of producing new examples of a thing, which will be indistinguishable from real ones, and

• a discriminator with an area that classifies the duplicate (whether the particular part of the organ is affected or not in a disease).

This architecture can be used in text, images, video, and audio.

There are various types of CNN, which are described in [33] (Table 1.1). Currently, coronavirus (COVID-19) is a fatal disease. By using deep learning we can predict the rate of the disease and which area is affected most. From Fig. 1.1 [36], we show the artificial intelligence (AI) procedure used for coronavirus. Fig. 1.2 introduces the procedure of training and testing of data. Figs. 1.3, 1.5 and 1.6 , describe the SqueezeNet and Fire model architecture, and Fig. 1.4 shows the GAN architecture. Sections 2 and 3 introduce the encryption as well as decryption of medical images to preserve authenticity. For this reason, no one can alter the patient's personal data, which may compromise the patient's medical information.

In today’s world, the movement of information utilizing the Internet is developing quickly. Thus many users can transfer business reports and significant data, for example, by utilizing the web. Security is a significant issue when transferring information utilizing the web because unapproved individuals can hack into the information for various reasons. In data storage, cryptography and steganography are the most utilized methods for sending delicate and private data safely.

An exceptional mainstream procedure to secure significant data over the Internet is the cryptography technique. In this strategy the information takes on a structure that can only be perceived by the proposed beneficiary. Because the coded information is in an unrecognized structure, it is open to the possibility of attack. Another security strategy, i.e., data cover-up, is likewise a generally utilized method that discourages the aggressor by disguising the data inside the bearer. This approach provides higher security and can guarantee message delivery. Steganography is the practice of hiding information within other less secret information. It is a Greek word: stegano infers covered or concealed and graphy infers writing. There are many ways to hide information, for instance, propelled pictures, chronicles, sound reports, and other PC records; however, modernized pictures are the most notable.

Table 1.1