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Automating the Design of Deep Learning Models Using Neural Architecture Search for Medical Image Classification.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Automating the Design of Deep Learning Models Using Neural Architecture Search for Medical Image Classification./
Author:	Balamurugan, Ridhanya Sree.
Description:	1 online resource (119 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-12.
Contained By:	Masters Abstracts International85-12.
Subject:	Biomedical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798383057056

Automating the Design of Deep Learning Models Using Neural Architecture Search for Medical Image Classification.
Balamurugan, Ridhanya Sree.

Automating the Design of Deep Learning Models Using Neural Architecture Search for Medical Image Classification. - 1 online resource (119 pages)

Source: Masters Abstracts International, Volume: 85-12.

Thesis (M.S.)--North Dakota State University, 2024.

Includes bibliographical references

esigning Deep Learning (DL) models for medical image classification tasks poses significant challenges, demanding substantial expertise owing to the intricate nature and critical importance of the undertaking. Creating a DL model tailored for such purposes entails iterative processes of designing, implementing, and fine-tuning algorithms to achieve optimal performance. To mitigate these difficulties, Neural Architecture Search (NAS) has risen as a key field to generate the most effective DL models automatically. However, much of the previous studies involving NAS focus on automating the design of DL models for well-established datasets such as CIFAR-10 and ImageNet. This technique should also be extended to medical image datasets where detecting crucial features accurately in medical images is essential to detect specific illnesses correctly. Therefore, in this study, we investigate NAS to autonomously design best performing DL models for skin lesion detection, thereby demonstrating its usefulness for additional medical image classification endeavours.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798383057056Subjects--Topical Terms:

588770
Biomedical engineering.
Subjects--Index Terms:

Deep LearningIndex Terms--Genre/Form:

554714
Electronic books.

Automating the Design of Deep Learning Models Using Neural Architecture Search for Medical Image Classification.
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