Polycystic Ovarian Syndrome Identification through Self-Attention Guided Convolutional Neural Network

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dc.contributor.authorTiwari, Shamik
dc.contributor.authorMaheshwari, Piyush
dc.date.accessioned2025-05-22T12:39:34Z
dc.date.available2025-05-22T12:39:34Z
dc.date.issued2023
dc.description.abstractPolycystic Ovarian Syndrome (PCOS) is a hormonal disorder that impacts women during their reproductive years, marked by indicators like multiple ovarian follicles or cysts that can be visualized through ultrasound imaging. Convolution Neural Networks (ConvNets) have been enhanced with self-attention mechanisms to improve their efficacy across a variety of computer vision applications, according to researchers. This study uses self-attention to improve the effectiveness of a ConvNet classifier in classifying PCOS, yielding a superior 99% accuracy, exceeding the 96% accuracy of a regular ConvNet classifier.
dc.identifier.citationTiwari, S., Maheshwari, P. and 2023 24th International Arab Conference on Information Technology (ACIT) Ajman, United Arab Emirates 2023 Dec. 6 - 2023 Dec. 8 (2023) “Polycystic Ovarian Syndrome Identification Through Self-Attention Guided Convolutional Neural Network,” in 2023 24th International Arab Conference on Information Technology (ACIT), pp. 1–6.
dc.identifier.doihttps://doi.org/10.1109/ACIT58888.2023.10453748.
dc.identifier.issn2831-4948
dc.identifier.urihttps://bspace.buid.ac.ae/handle/1234/3097
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofseries2023 24th International Arab Conference on Information Technology (ACIT)1-6
dc.subjectPCOS; ConvNet; Sellf-attention ConvNet; Classification.
dc.titlePolycystic Ovarian Syndrome Identification through Self-Attention Guided Convolutional Neural Network
dc.typeArticle
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