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dc.contributor.authorBalamurugan, Ridhanya
dc.description.abstractDesigning 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.en_US
dc.publisherNorth Dakota State Universityen_US
dc.rightsNDSU policy 190.6.2en_US
dc.titleAutomating the design of deep learning models using neural architecture search for medical image classificationen_US
dc.typeThesisen_US
dc.date.accessioned2024-08-09T17:57:19Z
dc.date.available2024-08-09T17:57:19Z
dc.date.issued2024
dc.identifier.urihttps://hdl.handle.net/10365/33949
dc.subjectAutoMLen_US
dc.subjectDeep Learningen_US
dc.subjectMachine Learningen_US
dc.subjectNeural Architecture Searchen_US
dc.subjectReinforcement Learningen_US
dc.rights.urihttps://www.ndsu.edu/fileadmin/policy/190.pdfen_US
ndsu.degreeMaster of Science (MS)en_US
ndsu.collegeEngineeringen_US
ndsu.departmentElectrical and Computer Engineeringen_US
ndsu.advisorTida, Umamaheswara Rao


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