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988			_aSpringer_Engineering_2020
999			_c114376 _d114376 _x1
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020			_a9789811389306
024	7		_a10.1007/978-981-13-8930-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aRC78.7 _b2020 EB
245	0	0	_aHybrid machine intelligence for medical image analysis _cedited by Siddhartha Bhattacharyya, Debanjan Konar, Jan Platos, Chinmoy Kar, Kalpana Sharma.
250			_aFirst edition
264		1	_aSingapore _bSpringer Singapore : _bImprint: Springer _c2020
300			_a1 recurso en línea (XVI, 293 páginas) _b179 ilustraciones, 114 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aStudies in Computational Intelligence _x1860-949X _v841
490	0		_aEngineering (Springer-11647)
505	0		_aPreface -- 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	3		_aThe 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		7	_2embne _aDiagnóstico por imagen _9139972
650		7	_2embne _aInteligencia artificial _9413115
650		7	_2embne _aAprendizaje automático _9166090
700	1		_aBhattacharyya, Siddhartha, _d1975- _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt _9100498
700	1		_aKonar, Debanjan _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aPlatos, Jan _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aKar, Chinmoy _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aSharma, Kalpana _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9789811389290
776	0	8	_iPrinted edition: _z9789811389313
776	0	8	_iPrinted edition: _z9789811389320
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-13-8930-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
998			_aSI _cm _dz _feng _ggw _h0 _b12/2019 _eel _zSI