Toward a Generic Multi-modal Medical Data Representation Model

K. M. Swaroopa; Nancy Kaur; Girija Chetty

doi:10.1007/978-981-99-5088-1_33

Toward a Generic Multi-modal Medical Data Representation Model

K. M. Swaroopa, Nancy Kaur, Girija Chetty

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

Abstract

This paper presents a generic multi-modal medical data representation model, based on utilizing the knowledge from the abundance of medical data in the publicly available medical imaging databases. Using novel deep learning techniques, this paper proposes an AI model that is technically capable of capturing characteristics of complex health conditions. The findings from this work based on extensive experimental work allows the development of robust and automatic detection of gliomas, an aggressive form of brain tumors. This model can provide significant benefits to the wide medical AI community and stimulate development and benefit universal health care in the long term.

Original language	English
Title of host publication	Advances in IoT and Security with Computational Intelligence - Proceedings of ICAISA 2023
Editors	Anurag Mishra, Deepak Gupta, Girija Chetty
Place of Publication	Netherlands
Publisher	Springer
Pages	385-394
Number of pages	10
Volume	2
ISBN (Print)	9789819950874
DOIs	https://doi.org/10.1007/978-981-99-5088-1_33
Publication status	Published - 2023
Event	International Conference on Advances in IoT, Security with AI, ICAISA 2023 - New Delhi, India Duration: 24 Mar 2023 → 25 Mar 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	756 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Advances in IoT, Security with AI, ICAISA 2023
Country/Territory	India
City	New Delhi
Period	24/03/23 → 25/03/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-981-99-5088-1_33

Cite this

Swaroopa, K. M., Kaur, N., & Chetty, G. (2023). Toward a Generic Multi-modal Medical Data Representation Model. In A. Mishra, D. Gupta, & G. Chetty (Eds.), Advances in IoT and Security with Computational Intelligence - Proceedings of ICAISA 2023 (Vol. 2, pp. 385-394). (Lecture Notes in Networks and Systems; Vol. 756 LNNS). Springer. https://doi.org/10.1007/978-981-99-5088-1_33

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title = "Toward a Generic Multi-modal Medical Data Representation Model",

abstract = "This paper presents a generic multi-modal medical data representation model, based on utilizing the knowledge from the abundance of medical data in the publicly available medical imaging databases. Using novel deep learning techniques, this paper proposes an AI model that is technically capable of capturing characteristics of complex health conditions. The findings from this work based on extensive experimental work allows the development of robust and automatic detection of gliomas, an aggressive form of brain tumors. This model can provide significant benefits to the wide medical AI community and stimulate development and benefit universal health care in the long term.",

keywords = "AI, Deep learning, Glioma, Medical imaging, Multi-modal",

author = "Swaroopa, {K. M.} and Nancy Kaur and Girija Chetty",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Advances in IoT, Security with AI, ICAISA 2023 ; Conference date: 24-03-2023 Through 25-03-2023",

year = "2023",

doi = "10.1007/978-981-99-5088-1_33",

language = "English",

isbn = "9789819950874",

volume = "2",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer",

pages = "385--394",

editor = "Anurag Mishra and Deepak Gupta and Girija Chetty",

booktitle = "Advances in IoT and Security with Computational Intelligence - Proceedings of ICAISA 2023",

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}

Swaroopa, KM, Kaur, N & Chetty, G 2023, Toward a Generic Multi-modal Medical Data Representation Model. in A Mishra, D Gupta & G Chetty (eds), Advances in IoT and Security with Computational Intelligence - Proceedings of ICAISA 2023. vol. 2, Lecture Notes in Networks and Systems, vol. 756 LNNS, Springer, Netherlands, pp. 385-394, International Conference on Advances in IoT, Security with AI, ICAISA 2023, New Delhi, India, 24/03/23. https://doi.org/10.1007/978-981-99-5088-1_33

Toward a Generic Multi-modal Medical Data Representation Model. / Swaroopa, K. M.; Kaur, Nancy; Chetty, Girija.
Advances in IoT and Security with Computational Intelligence - Proceedings of ICAISA 2023. ed. / Anurag Mishra; Deepak Gupta; Girija Chetty. Vol. 2 Netherlands: Springer, 2023. p. 385-394 (Lecture Notes in Networks and Systems; Vol. 756 LNNS).

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

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AU - Kaur, Nancy

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AB - This paper presents a generic multi-modal medical data representation model, based on utilizing the knowledge from the abundance of medical data in the publicly available medical imaging databases. Using novel deep learning techniques, this paper proposes an AI model that is technically capable of capturing characteristics of complex health conditions. The findings from this work based on extensive experimental work allows the development of robust and automatic detection of gliomas, an aggressive form of brain tumors. This model can provide significant benefits to the wide medical AI community and stimulate development and benefit universal health care in the long term.

KW - AI

KW - Deep learning

KW - Glioma

KW - Medical imaging

KW - Multi-modal

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UR - https://icaisa2023.com/

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DO - 10.1007/978-981-99-5088-1_33

M3 - Conference contribution

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SN - 9789819950874

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T3 - Lecture Notes in Networks and Systems

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BT - Advances in IoT and Security with Computational Intelligence - Proceedings of ICAISA 2023

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A2 - Gupta, Deepak

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PB - Springer

CY - Netherlands

T2 - International Conference on Advances in IoT, Security with AI, ICAISA 2023

Y2 - 24 March 2023 through 25 March 2023

ER -

Toward a Generic Multi-modal Medical Data Representation Model

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