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Hybrid machine intelligence for medi...

Bhattacharyya, Siddhartha.

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Hybrid machine intelligence for medical image analysis

Record Type:	Electronic resources : Monograph/item
Title/Author:	Hybrid machine intelligence for medical image analysis/ edited by Siddhartha Bhattacharyya ... [et al.].
other author:	Bhattacharyya, Siddhartha.
Published:	Singapore :Springer Singapore : : 2020.,
Description:	xvi, 293 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-981-13-8930-6
ISBN:	9789811389306

Hybrid machine intelligence for medical image analysis
Hybrid machine intelligence for medical image analysis[electronic resource] /edited by Siddhartha Bhattacharyya ... [et al.]. - Singapore :Springer Singapore :2020. - xvi, 293 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.8411860-949X ;. - Studies in computational intelligence ;v.841..

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.

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.

ISBN: 9789811389306

Standard No.: 10.1007/978-981-13-8930-6doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Hybrid machine intelligence for medical image analysis
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245 0 0 $a Hybrid machine intelligence for medical image analysis $h [electronic resource] / $c edited by Siddhartha Bhattacharyya ... [et al.].
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a xvi, 293 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.841
505 0 $a 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.
520 $a 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.
650 0 $a Machine learning. $3 533906
650 0 $a Computational intelligence. $3 595739
650 1 4 $a Signal, Image and Speech Processing. $3 891073
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Image Processing and Computer Vision. $3 891070
650 2 4 $a Pattern Recognition. $3 891045
700 1 $a Bhattacharyya, Siddhartha. $3 2179171
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