Hybrid machine intelligence for medical image analysis / Siddhartha Bhattacharyya, Debanjan Konar, Jan Platos, Chinmoy Kar, Kalpana Sharma, editors.
Bhattacharyya, Siddhartha, 1975- editor.; Konar, Debanjan, editor.; Platos, Jan, editor.; Kar, Chinmoy, editor.; Sharma, Kalpana (Head of Computer Science and Engineering), editor.
2020
Q325.5
Available Online

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Title Hybrid machine intelligence for medical image analysis / Siddhartha Bhattacharyya, Debanjan Konar, Jan Platos, Chinmoy Kar, Kalpana Sharma, editors.
ISBN 9789811389306 (electronic book)
9811389306 (electronic book)
9789811389290
DOI https://doi.org/10.1007/978-981-13-8930-6
https://doi.org/10.1007/978-981-13-8
Published Singapore : Springer, 2020.
Language English
Description 1 online resource (xvi, 293 pages) : illustrations.
Item Number 10.1007/978-981-13-8930-6 doi
10.1007/978-981-13-8
Call Number Q325.5
Dewey Decimal Classification 006.3/1
Summary The book discusses the impact of machine learning and computational intelligent algorithms on medical image data processing, and introduces the latest trends in machine learning technologies and computational intelligence for intelligent medical image analysis. The topics covered include automated region of interest detection of magnetic resonance images based on center of gravity; brain tumor detection through low-level features detection; automatic MRI image segmentation for brain tumor detection using the multi-level sigmoid activation function; and computer-aided detection of mammographic lesions using convolutional neural networks.
Note Includes author index.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed August 29, 2019).
Added Author Bhattacharyya, Siddhartha, 1975- editor.
Konar, Debanjan, editor.
Platos, Jan, editor.
Kar, Chinmoy, editor.
Sharma, Kalpana (Head of Computer Science and Engineering), editor.
Series Studies in computational intelligence ; v. 841.
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Record Appears in Online Resources > Ebooks
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Preface
Introduction
Brain Tumor Segmentation from T1 Weighted MRI Images Using Rough Set Reduct and Quantum Inspired Particle Swarm Optimization
Automated Region of Interest detection of Magnetic Resonance (MR) images by Center of Gravity (CoG)
Brain tumors detection through low level features detection and rotation estimation
Automatic MRI Image Segmentation for Brain tumors detection using Multilevel Sigmoid Activation (MUSIG) function
Automatic Segmentation of pulmonary nodules in CT Images for Lung Cancer detection using self-supervised Neural Network Architecture
A Hierarchical Fused Fuzzy Deep Neural Network for MRI Image Segmentation and Brain Tumor Classification
Computer Aided Detection of Mammographic Lesions using Convolutional Neural Network (CNN)
Conclusion.

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