SMOTE-Based Automated PCOS Prediction Using Lightweight Deep Learning Models

Moreover, a performance comparison is also carried out with other state-of-the-art PCOS detection studies and methods, which validates the better performance of the proposed model. Thus, the proposed model provides the greatest performance, which can lead to a reduction in the number of failed pregnancies and help in finding PCOS in the early stages.

Authors

Louai A. Maghrabi (ORCID: https://orcid.org/0000-0001-8513-0645)
Ishfaq Ahmad Khaja (ORCID: https://orcid.org/0000-0001-9173-3952)
Musheer Ahmad (ORCID: https://orcid.org/0000-0002-4915-9325)
Lamees A. Maghrabi
Rumman Ahmad

Institutions

Soliman Fakeeh Hospital (SA)
University of Business and Technology (SA)
Jamia Millia Islamia (IN)

Publication Details

Journal: Diagnostics
Published: 2024-10-05
DOI: https://doi.org/10.3390/diagnostics14192225
Citations: 21
Primary Topic: Ovarian function and disorders
Type: article
Field-Weighted Citation Impact: 11.28

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article

SMOTE-Based Automated PCOS Prediction Using Lightweight Deep Learning Models

Louai A. Maghrabi, Ishfaq Ahmad Khaja, Musheer Ahmad, Lamees A. Maghrabi et al.

Oct 5, 2024

21 citations

Diagnostics

Ovarian function and disorders

11.28

article

SMOTE-Based Automated PCOS Prediction Using Lightweight Deep Learning Models

Louai A. Maghrabi, Ishfaq Ahmad Khaja, Musheer Ahmad, Lamees A. Maghrabi, Rumman Ahmad

October 5, 2024article en

21 citations

Abstract

Moreover, a performance comparison is also carried out with other state-of-the-art PCOS detection studies and methods, which validates the better performance of the proposed model. Thus, the proposed model provides the greatest performance, which can lead to a reduction in the number of failed pregnancies and help in finding PCOS in the early stages.

DiagnosticsVol. 14(19)

Soliman Fakeeh Hospital (SA), University of Business and Technology (SA), Jamia Millia Islamia (IN)

Good health and well-being, Gender equality

Openalex Percentile: Top 1%

Ovarian function and disorders

11.28

DOI Open Access PDF OpenAlex

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