| Description |
1 online resource (362 p.) |
| Series |
Computational Methods for Industrial Applications Ser |
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Computational Methods for Industrial Applications Ser
|
| Contents |
Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- About the Editors -- Contributors -- Chapter 1 A framework for a virtual reality-based medical support system -- 1.1 Introduction -- 1.2 Background -- 1.2.1 What is VR? -- 1.2.2 VR in healthcare education -- 1.2.3 Utilization of VR in the medical sector -- 1.3 Stakeholders of the system -- 1.3.1 Desirements of the stakeholders -- 1.4 System purpose and concept of operations -- 1.4.1 Concept of operations -- 1.4.1.1 Current and planned system -- 1.4.1.2 Functions of the system |
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1.4.1.3 Critical system requirements -- 1.4.2 Context diagram -- 1.4.2.1 First terminator -- 1.4.2.2 Second terminator -- 1.4.2.3 Third terminator -- 1.4.2.4 Fourth terminator -- 1.5 Systems requirements and use cases -- 1.5.1 Use cases -- 1.5.2 Block definition diagrams -- 1.6 Technical performance measure (TPM) -- 1.7 Conclusion -- References -- Chapter 2 ConvMax: Classification of COVID-19, pneumonia, and normal lungs from X-ray images using CNN with modified max-pooling layer -- 2.1 Introduction -- 2.2 Literature review -- 2.3 Proposed work -- 2.3.1 Proposed methodology |
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2.3.2 Dataset collection -- 2.3.3 Novel contributions of this study -- 2.3.4 System flow and related concepts -- 2.3.5 Proposed CNN architecture -- 2.4 Results and discussion -- 2.5 Conclusion -- References -- Chapter 3 Biorthogonal filter-based algorithm for denoising and segmentation of fundus images -- 3.1 Introduction -- 3.1.1 Motivation -- 3.1.2 Major contribution -- 3.1.3 Outcomes -- 3.1.4 Chapter organization -- 3.2 Literature review -- 3.2.1 Denoising of medical images -- 3.2.2 Segmentation techniques for medical images -- 3.3 Performance evaluation -- 3.3.1 Denoising performance metrics |
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3.3.2 Segmentation performance metrics -- 3.4 Biorthogonal wavelet transforms and filters -- 3.5 Investigational results -- 3.5.1 Noise suppression in images using the wavelet transform -- 3.5.2 Contrast enhancement of the images for improved segmentation -- 3.5.3 Findings -- 3.6 Discussion and conclusions -- References -- Chapter 4 Deep learning-based automatic detection of breast lesions on ultrasound images -- 4.1 Introduction -- 4.2 Methodology -- 4.2.1 Block diagram of proposed method -- 4.2.2 Breast ultrasound dataset -- 4.2.3 Preprocessing -- 4.2.3.1 Overview |
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4.2.3.2 Block diagram of the proposed speckle reduction method -- 4.2.3.3 Speckle reduction by circular hybrid median filter technique -- 4.2.3.4 Algorithm -- 4.2.3.5 Performance indices -- 4.2.4 Segmentation -- 4.2.4.1 Need for image segmentation -- 4.2.4.2 CNN-based image segmentation -- 4.2.4.3 Residual network -- 4.2.4.4 Implementation -- 4.2.4.5 Analyzing the network -- 4.2.4.6 Performance indices -- 4.3 Results and discussions -- 4.3.1 Preprocessing results -- 4.3.2 Segmentation results -- 4.4 Conclusion -- References |
| Notes |
Description based upon print version of record |
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Chapter 5 Heart disease prediction using enhanced machine learning techniques |
| Subject |
Manufacturing processes -- Technological innovations
|
|
Machine learning -- Industrial applications
|
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Medical technology -- Automation
|
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Machine learning -- Industrial applications
|
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Manufacturing processes -- Technological innovations
|
| Form |
Electronic book
|
| Author |
Reddy, C. Kishor Kumar
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Nguyen, Nhu Gia
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Bhushan, Megha
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Kumar, Ashok
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Mohd Hanafiah, Marlia
|
| ISBN |
9781000828870 |
|
1000828875 |
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