Deep learning strategy for predicting liver cancer using convolutional neural network algorithm (Record no. 18252)

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control field 20221117113031.0
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040 ## - CATALOGING SOURCE
Original cataloging agency AIKTC-KRRC
Transcribing agency AIKTC-KRRC
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9 (RLIN) 19103
Author Nagalpara, Sirajali
245 ## - TITLE STATEMENT
Title Deep learning strategy for predicting liver cancer using convolutional neural network algorithm
250 ## - EDITION STATEMENT
Volume, Issue number Vol.7(3), May-Jun
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. New Delhi
Name of publisher, distributor, etc. Associated Management Consultants
Year 2022
300 ## - PHYSICAL DESCRIPTION
Pagination 43-49p.
520 ## - SUMMARY, ETC.
Summary, etc. One of the common types of cancer is liver cancer, early detection and diagnosis of which are critical. Discovery, decision, and aggressive therapy can prevent most cancer deaths. We use data mining approaches (Convolutional Neural Networks) to build prediction models for liver cancer with the most widely used statistical analysis methodology. Around 579 records and 10 variables were included in the data collection. The model was built, evaluated, and compared using a k-fold cross-validation process. CNN was the best accurate predictor for this domain with a test set accuracy of 100%.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
9 (RLIN) 4622
Topical term or geographic name entry element Computer Engineering
700 ## - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 19104
Co-Author Patel, Bhavesh M.
773 0# - HOST ITEM ENTRY
Place, publisher, and date of publication New Delhi Associated Management Consultants
Title Indian Journal of Computer Science
International Standard Serial Number 2456-4133
856 ## - ELECTRONIC LOCATION AND ACCESS
URL http://www.indianjournalofcomputerscience.com/index.php/tcsj/article/view/171270
Link text Click here
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Koha item type Articles Abstract Database
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          School of Engineering & Technology School of Engineering & Technology Archieval Section 2022-11-17 2022-2140 2022-11-17 2022-11-17 Articles Abstract Database
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