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  1. Book ; Online ; E-Book: Diagnosis of Neurological Disorders Based on Deep Learning Techniques

    Chaki, Jyotismita

    2023  

    Author's details edited by Jyotismita Chaki
    Keywords Artificial intelligence/Medical applications ; Nervous system/Diseases/Diagnosis
    Subject code 610.285
    Language English
    Size 1 online resource (237 pages)
    Publisher CRC Press
    Publishing place Boca Raton, FL
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    ISBN 1-00-331545-3 ; 1-003-31545-3 ; 1-000-87217-3 ; 9781032325231 ; 978-1-00-331545-2 ; 978-1-003-31545-2 ; 978-1-000-87217-0 ; 1032325232
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Book ; Online ; E-Book: Brain tumor MRI image segmentation using deep learning techniques

    Chaki, Jyotismita

    2022  

    Author's details edited by Jyotismita Chaki
    Keywords Brain/Tumors/Diagnosis ; Brain/Magnetic resonance imaging
    Subject code 616.8047548
    Language English
    Size 1 online resource (260 pages)
    Publisher Elsevier
    Publishing place London, England ; San Diego, California
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    ISBN 0-323-98395-2 ; 9780323911719 ; 978-0-323-98395-2 ; 0323911714
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  3. Book ; Online ; E-Book: Current applications of deep learning in cancer diagnostics

    Chaki, Jyotismita / Ucar, Aysegul

    2023  

    Author's details edited by Jyotismita Chaki, Aysegul Ucar
    Keywords Electronic books
    Language English
    Size 1 Online-Ressource (xix, 167 Seiten), Illustrationen, Diagramme
    Edition First edition
    Publisher CRC Press
    Publishing place Boca Raton
    Publishing country United States
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    HBZ-ID HT021721347
    ISBN 978-1-003-27700-2 ; 978-1-000-83615-8 ; 9781032233857 ; 9781032223193 ; 1-003-27700-4 ; 1-000-83615-0 ; 1032233850 ; 1032223197
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  4. Book: Diagnosis of Neurological Disorders Based on Deep Learning Techniques

    Chaki, Jyotismita

    2023  

    Abstract: This book is based on deep learning approaches used for the diagnosis of neurological disorders, including basics of deep learning algorithms using diagrams, data tables, and practical examples, for diagnosis of neurodegenerative and neurodevelopmental ... ...

    Author's details Jyotismita Chaki, PhD, is an Associate Professor in School of Computer Science and Engineering at Vellore Institute of Technology, Vellore, India. She gained her PhD (Engg.) from Jadavpur University, Kolkata, India. Her research interests include computer vision and image processing, pattern recognition, medical imaging, artificial intelligence, and machine learning. Jyotismita has authored more than 40 international conference and journal papers and is the author and editor of more than eight books. Currently, she is the Academic Editor of PLOS One journal and PeerJ Computer Science journal and Associate Editor of IET Image Processing journal, Array journal, and Machine Learning with Applications journal
    Abstract This book is based on deep learning approaches used for the diagnosis of neurological disorders, including basics of deep learning algorithms using diagrams, data tables, and practical examples, for diagnosis of neurodegenerative and neurodevelopmental disorders. It includes application of feed-forward neural networks, deep generative models, convolutional neural networks, graph convolutional networks, and recurrent neural networks in the field of diagnosis of neurological disorders. Along wi...
    Keywords Pre-processing ; Internet of Things ; Machine Learning ; Psychiatric Disorders ; Neurodevelopmental ; Neurodegenerative ; DNN Model ; LBP. ; CNN Model ; Max Pooling Layer ; DNN ; Deep Learning Methods ; Deep Learning Techniques ; ADNI ; EEG Signal ; Convolutional Layers ; CNN Architecture ; Convolution Layer ; Deep Learning Models ; Mri Image ; Autism Spectrum Disorder ; Brain Mri Image ; Data Pre-processing Techniques ; Dice Similarity Coefficient ; Actigraphy Analysis ; Deep Learning Architecture ; Roc Curve ; Dl Algorithm ; Alzheimer’s Disease ; Ad ; Pre-processing Techniques
    Language English
    Size 222 p.
    Edition 1
    Publisher Taylor & Francis
    Document type Book
    Note PDA Manuell_19
    Format 235 x 158 x 21
    ISBN 9781032325231 ; 1032325232
    Database PDA

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  5. Book ; Online ; E-Book: Smart biosensors in medical care

    Chaki, Jyotismita / Dey, Nilanjan / De, Debashish

    2020  

    Author's details edited by Jyotismita Chaki, Nilanjan Dey, Debashish De
    Keywords Biosensors
    Subject code 610.28
    Language English
    Size 1 online resource (250 pages)
    Publisher Academic Press
    Publishing place London, England
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    ISBN 0-12-820936-4 ; 0-12-820781-7 ; 978-0-12-820936-3 ; 978-0-12-820781-9
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  6. Book: Artificial Intelligence for Coronavirus Outbreak

    Fong, Simon James / Chaki, Jyotismita / Dey, Nilanjan

    (SpringerBriefs in Applied Sciences and Technology)

    2021  

    Abstract: This book examines how the wonders of AI have contributed to the battle against COVID-19. Just as history repeats itself, so do epidemics and pandemics. In the face of the novel coronavirus disease, COVID-19, the book explores whether, in this digital ... ...

    Author's details Simon Fong graduated from La Trobe University, Australia, with a 1st Class Honors B.E. Computer Systems degree and a Ph.D. Computer Science degree in 1993 and 1998, respectively. Simon is now working as an Associate Professor at the Computer and Information Science Department of the University of Macau. He is a co-founder of the Data Analytics and Collaborative Computing Research Group in the Faculty of Science and Technology. Prior to his academic career, Simon took up various managerial and technical posts, such as Systems Engineer, IT Consultant, and E-commerce Director in Australia and Asia. Dr. Fong has published over 380 international conference and peer-reviewed journal papers, mostly in the areas of data mining, data stream mining, big data analytics, meta-heuristics optimization algorithms, and their applications. He serves on the editorial boards of the Journal of Network and Computer Applications of Elsevier, IEEE IT Professional Magazine, and various special issues of SC
    Series title SpringerBriefs in Applied Sciences and Technology
    Abstract This book examines how the wonders of AI have contributed to the battle against COVID-19. Just as history repeats itself, so do epidemics and pandemics. In the face of the novel coronavirus disease, COVID-19, the book explores whether, in this digital era where artificial intelligence is successfully applied in all areas of industry, we are doing any better than our ancestors did in dealing with pandemics. One of the most contagious diseases ever known, COVID-19 is spreading like wildfire aro...
    Keywords FINSA ; FIND ; MHMA060 ; datamining ; DeepLearning ; DataAnalytics ; DataScience ; PublicHealth ; COVID-2019 ; ArtificialIntelligence ; DiseaseSurveillance ; EpidemicMonitoringandControl ; Coronavirus ; Artificial Intelligence ; Disease Surveillance ; Epidemic Monitoring and Control ; Public Health ; Data Mining ; Deep Learning ; Data Analytics ; Data Science
    Language English
    Size 88 p.
    Edition 1
    Publisher Springer Nature Singapore
    Document type Book
    Note PDA Manuell_9
    Format 155 x 235 x 6
    ISBN 9789811559358 ; 981155935X
    Database PDA

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  7. Article ; Online: An automatic system for extracting figure-caption pair from medical documents: a six-fold approach.

    Chaki, Jyotismita

    PeerJ. Computer science

    2023  Volume 9, Page(s) e1452

    Abstract: Background: Figures and captions in medical documentation contain important information. As a result, researchers are becoming more interested in obtaining published medical figures from medical papers and utilizing the captions as a knowledge source.!## ...

    Abstract Background: Figures and captions in medical documentation contain important information. As a result, researchers are becoming more interested in obtaining published medical figures from medical papers and utilizing the captions as a knowledge source.
    Methods: This work introduces a unique and successful six-fold methodology for extracting figure-caption pairs. The A-torus wavelet transform is used to retrieve the first edge from the scanned page. Then, using the maximally stable extremal regions connected component feature, text and graphical contents are isolated from the edge document, and multi-layer perceptron is used to successfully detect and retrieve figures and captions from medical records. The figure-caption pair is then extracted using the bounding box approach. The files that contain the figures and captions are saved separately and supplied to the end useras theoutput of any investigation. The proposed approach is evaluated using a self-created database based on the pages collected from five open access books: Sergey Makarov, Gregory Noetscher and Aapo Nummenmaa's book "Brain and Human Body Modelling 2021", "Healthcare and Disease Burden in Africa" by Ilha Niohuru, "All-Optical Methods to Study Neuronal Function" by Eirini Papagiakoumou, "RNA, the Epicenter of Genetic Information" by John Mattick and Paulo Amaral and "Illustrated Manual of Pediatric Dermatology" by Susan Bayliss Mallory, Alanna Bree and Peggy Chern.
    Results: Experiments and findings comparing the new method to earlier systems reveal a significant increase in efficiency, demonstrating the suggested technique's robustness and efficiency.
    Language English
    Publishing date 2023-07-26
    Publishing country United States
    Document type Journal Article
    ISSN 2376-5992
    ISSN (online) 2376-5992
    DOI 10.7717/peerj-cs.1452
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Healthy-unhealthy animal detection using semi-supervised generative adversarial network.

    Almal, Shubh / Bagepalli, Apoorva Reddy / Dutta, Prajjwal / Chaki, Jyotismita

    PeerJ. Computer science

    2023  Volume 9, Page(s) e1250

    Abstract: Background: Animal illness is a disturbance in an animal's natural condition that disrupts or changes critical functions. Concern over animal illnesses stretches back to the earliest human interactions with animals and is mirrored in early religious and ...

    Abstract Background: Animal illness is a disturbance in an animal's natural condition that disrupts or changes critical functions. Concern over animal illnesses stretches back to the earliest human interactions with animals and is mirrored in early religious and magical beliefs. Animals have long been recognized as disease carriers. Man has most likely been bitten, stung, kicked, and gored by animals for as long as he has been alive; also, early man fell ill or died after consuming the flesh of deceased animals. Man has recently learned that numerous invertebrates are capable of transferring disease-causing pathogens from man to man or from other vertebrates to man. These animals, which function as hosts, agents, and carriers of disease, play a significant role in the transmission and perpetuation of human sickness. Thus, there is a need to detect unhealthy animals from a whole group of animals.
    Methods: In this study, a deep learning-based method is used to detect or separate out healthy-unhealthy animals. As the dataset contains a smaller number of images, an image augmentation-based method is used prior to feed the data in the deep learning network. Flipping, scale-up, sale-down and orientation is applied in the combination of one to four to increase the number of images as well as to make the system robust from these variations. One fuzzy-based brightness correction method is proposed to correct the brightness of the image. Lastly, semi-supervised generative adversarial network (SGAN) is used to detect the healthy-unhealthy animal images. As per our knowledge, this is the first article which is prepared to detect healthy-unhealthy animal images.
    Results: The outcome of the method is tested on augmented COCO dataset and achieved 91% accuracy which is showing the efficacy of the method.
    Conclusions: A novel two-fold animal healthy-unhealthy detection system is proposed in this study. The result gives 91.4% accuracy of the model and detects the health of the animals in the pictures accurately. Thus, the system improved the literature on healthy-unhealthy animal detection techniques. The proposed approach may effortlessly be utilized in many computer vision systems that could be confused by the existence of a healthy-unhealthy animal.
    Language English
    Publishing date 2023-02-15
    Publishing country United States
    Document type Journal Article
    ISSN 2376-5992
    ISSN (online) 2376-5992
    DOI 10.7717/peerj-cs.1250
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Data Tagging in Medical Images: A Survey of the State-of-Art.

    Chaki, Jyotismita / Dey, Nilanjan

    Current medical imaging

    2020  Volume 16, Issue 10, Page(s) 1214–1228

    Abstract: A huge amount of medical data is generated every second, and a significant percentage of the data are images that need to be analyzed and processed. One of the key challenges in this regard is the recovery of the data of medical images. The medical image ...

    Abstract A huge amount of medical data is generated every second, and a significant percentage of the data are images that need to be analyzed and processed. One of the key challenges in this regard is the recovery of the data of medical images. The medical image recovery procedure should be done automatically by the computers that are the method of identifying object concepts and assigning homologous tags to them. To discover the hidden concepts in the medical images, the lowlevel characteristics should be used to achieve high-level concepts and that is a challenging task. In any specific case, it requires human involvement to determine the significance of the image. To allow machine-based reasoning on the medical evidence collected, the data must be accompanied by additional interpretive semantics; a change from a pure data-intensive methodology to a model of evidence rich in semantics. In this state-of-art, data tagging methods related to medical images are surveyed which is an important aspect for the recognition of a huge number of medical images. Different types of tags related to the medical image, prerequisites of medical data tagging, different techniques to develop medical image tags, different medical image tagging algorithms and different tools that are used to create the tags are discussed in this paper. The aim of this state-of-art paper is to produce a summary and a set of guidelines for using the tags for the identification of medical images and to identify the challenges and future research directions of tagging medical images.
    MeSH term(s) Algorithms ; Humans ; Semantics
    Language English
    Publishing date 2020-02-28
    Publishing country United Arab Emirates
    Document type Journal Article
    ISSN 1573-4056
    ISSN (online) 1573-4056
    DOI 10.2174/1573405616666200218130043
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: AI-Enabled Technologies that Fight the Coronavirus Outbreak

    Fong, Simon James / Dey, Nilanjan / Chaki, Jyotismita

    Artificial Intelligence for Coronavirus Outbreak

    Abstract: Development of innovative designs, new applications, new technologies and heavier investment in AI are continued to be seen every day However, with the sudden impact of COVID19, so severe and urgent around the world, adoption of AI is propelled to an ... ...

    Abstract Development of innovative designs, new applications, new technologies and heavier investment in AI are continued to be seen every day However, with the sudden impact of COVID19, so severe and urgent around the world, adoption of AI is propelled to an unprecedent level, because it helps to fight the virus pandemic by enabling one or more of the following possibilities: (1) autonomous everything, (2) pervasive knowledge, (3) assistive technology and (4) rational decision support
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #825061
    Database COVID19

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