Screen LibraryComputer vision and machine intelligence in medical image analysis : international symposium, ISCMM 2019 /
This book includes high-quality papers presented at the Symposium 2019, organised by Sikkim Manipal Institute of Technology (SMIT), in Sikkim from 26-27 February 2019. It discusses common research problems and challenges in medical image analysis, such as deep learning methods. It also discusses how...
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| Corporate Authors: | |
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| Group Author: | ; ; ; |
| Published: |
Springer,
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| Publisher Address: | Singapore : |
| Publication Dates: | [2020] |
| Literature type: | Book |
| Language: | English |
| Series: |
Advances in intelligent systems and computing,
volume 992 |
| Subjects: | |
| Summary: |
This book includes high-quality papers presented at the Symposium 2019, organised by Sikkim Manipal Institute of Technology (SMIT), in Sikkim from 26-27 February 2019. It discusses common research problems and challenges in medical image analysis, such as deep learning methods. It also discusses how these theories can be applied to a broad range of application areas, including lung and chest x-ray, breast CAD, microscopy and pathology. The studies included mainly focus on the detection of events from biomedical signals. |
| Carrier Form: | xii, 150 pages : illustrations (some color) ; 25 cm. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: |
9789811387975 9811387974 |
| Index Number: | R857 |
| CLC: | R445-532 |
| Call Number: | R445-532/I617-1/2019 |
| Contents: | Chapter 1. A Novel Method for Pneumonia Diagnosis from Chest X-Ray Images Using Deep Residual Learning with Separable Convolutional Networks -- Chapter 2. Identification of Neural Correlates of Face Recognition Using Machine Learning Approach -- Chapter 3. An Overview of Remote Photoplethysmography Methods for Vital Sign Monitoring -- Chapter 4. Fuzzy Inference System for Efficient Lung Cancer Detection -- Chapter 5. Medical Image Compression Scheme Using Number Theoretic Transform -- Chapter 6. The Retinal Blood Vessel Segmentation Using Expected Maximization Algorithm -- Chapter 7. Classif |