000 04338nam a22005775i 4500
001 978-3-030-96569-3
003 DE-He213
005 20240423125153.0
007 cr nn 008mamaa
008 220523s2022 sz | s |||| 0|eng d
020 _a9783030965693
_9978-3-030-96569-3
024 7 _a10.1007/978-3-030-96569-3
_2doi
050 4 _aR858-859.7
072 7 _aMBG
_2bicssc
072 7 _aUB
_2bicssc
072 7 _aMED117000
_2bisacsh
072 7 _aUXT
_2thema
082 0 4 _a610,285
_223
245 1 0 _aArtificial Intelligence for Innovative Healthcare Informatics
_h[electronic resource] /
_cedited by Shabir Ahmad Parah, Mamoon Rashid, Vijayakumar Varadarajan.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _aVI, 327 p. 94 illus., 79 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aSection 1: Medical Image Analysis using Artificial Intelligence -- Use of Deep Learning in Biomedical Imaging.-Detection of Breast Cancer Masses in Mammogram Images with Watershed Segmentation and Machine Learning Approach -- Cloud-based Glaucoma Diagnosis in Medical Imaging using Machine Learning -- Leucocytic Cell Nucleus Identification using Boundary Cell Detection algorithm with Dilation and Erosion based Morphometry -- Effective Prediction of Autism Using Ensemble Method.-Section 2: Artificial Intelligence (AI) Classification Models for COVID-19 Pandemic.-Automatic Classification of COVID-19 infected patients using Convolution Neural Network Models.-AI-Based Deep Random Forest Ensemble Model for Prediction of COVID-19 and Pneumonia from Chest X-Ray Images -- Section 3: Use of AI-Enabled IoT in Healthcare -- Internet of Things and Artificial Intelligence in Biomedical Systems.-Role of IoT in Healthcare Sector for Monitoring Diabetic Patients -- Section 4: Applications of Artificial Intelligence in Healthcare -- Low-Rank Representation based approach for subspace segmentation and clustering of biomedical image patterns.-Performance Comparison of Imputation Methods for Heart Disease Prediction -- Ayurnano: A solution towards herbal therapeutics using Artificial Intelligence approach -- Artificial Intelligence in Biomedical Education -- The Emergence of Natural Language Processing (NLP) Techniques in Healthcare AI -- Prospects and Difficulties of Artificial Intelligence (AI) Implementations in Naturopathy.
520 _aThere are several popular books published in Healthcare Computational Informatics like Computational Bioengineering and Bioinformatics (2020), Springer; Health Informatics (2017), Springer; Health Informatics Vision: From Data via Information to Knowledge (2019), IOS Press; Data Analytics in Biomedical Engineering and Healthcare (2020), Elsevier. However, in all these mentioned books, the challenges in Biomedical Imaging are solved in one dimension by use of any specific technology like Image Processing, Machine Learning or Computer Aided Systems. In this book, the book it has been attempted to bring all technologies related to computational analytics together and apply them on Biomedical Imaging.
650 0 _aMedical informatics.
650 0 _aArtificial intelligence.
650 0 _aImage processing.
650 0 _aMachine learning.
650 1 4 _aHealth Informatics.
650 2 4 _aArtificial Intelligence.
650 2 4 _aImage Processing.
650 2 4 _aMachine Learning.
700 1 _aParah, Shabir Ahmad.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aRashid, Mamoon.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aVaradarajan, Vijayakumar.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030965686
776 0 8 _iPrinted edition:
_z9783030965709
776 0 8 _iPrinted edition:
_z9783030965716
856 4 0 _uhttps://doi.org/10.1007/978-3-030-96569-3
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c175024
_d175024