國立高雄大學圖資館 |

Language: English

Back

Hybrid machine intelligence for medi...

Bhattacharyya, Siddhartha.

Hybrid machine intelligence for medical image analysis

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

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. 216..

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$q(electronic bk.)

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

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Hybrid machine intelligence for medical image analysis
LDR:02547nmm a2200349 a 4500 001 577648
003 DE-He213
005 20200220154055.0
006 m d
007 cr nn 008maaau
008 201203s2020 si s 0 eng d
020 $a 9789811389306$q(electronic bk.)
020 $a 9789811389290$q(paper)
024 7 $a 10.1007/978-981-13-8930-6 $2 doi
035 $a 978-981-13-8930-6
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a TTBM $2 bicssc
072 7 $a TEC008000 $2 bisacsh
072 7 $a TTBM $2 thema
072 7 $a UYS $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .H992 2020
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 188639
650 0 $a Computational intelligence. $3 210824
650 1 4 $a Signal, Image and Speech Processing. $3 273768
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Image Processing and Computer Vision. $3 274051
650 2 4 $a Pattern Recognition. $3 273706
700 1 $a Bhattacharyya, Siddhartha. $3 736923
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Studies in computational intelligence ; $v v. 216. $3 380871
856 4 0 $u https://doi.org/10.1007/978-981-13-8930-6
950 $a Engineering (Springer-11647)