International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12

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 Volume 12, Issue 12 (December 2025), Pages: 75-86

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 Original Research Paper

An automated approach for prostate cancer detection using CGAN data augmentation with a weighted ensemble of transfer learning models

 Author(s): 

 Raafat M. Munshi *

 Affiliation(s):

 Department of Medical Laboratory Technology (MLT), Faculty of Applied Medical Sciences, King Abdulaziz University, Rabigh, Saudi Arabia

 Full text

    Full Text - PDF

 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0000-0001-7696-0452

 Digital Object Identifier (DOI)

  https://doi.org/10.21833/ijaas.2025.12.008

 Abstract

Prostate cancer is one of the most common and lethal cancers among men worldwide, often developing without symptoms or with only minor signs, which makes early detection difficult. This study introduces a novel deep learning framework that integrates a Conditional Generative Adversarial Network (CGAN) with a weighted ensemble model consisting of ResNet (15%), Xception (50%), and Inception (35%). The model was trained and evaluated on the publicly available PROSTATEx image dataset, with performance measured using accuracy, precision, recall, and F1-score. The proposed weighted ensemble achieved 98.25% accuracy, 96.75% precision, 97.58% recall, and 96.65% F1-score, outperforming the soft voting ensemble, which obtained 95% accuracy, 92% precision, 94% recall, and 93% F1-score. Comparative analysis with state-of-the-art methods and cross-validation further confirmed the robustness of the proposed approach. These findings suggest that integrating CGAN with a weighted ensemble significantly enhances prostate cancer detection and holds strong potential for clinical application in early diagnosis and patient management.

 © 2025 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Prostate cancer, Deep learning, Generative adversarial network, Ensemble model, Medical imaging

 Article history

 Received 19 June 2025, Received in revised form 16 October 2025, Accepted 16 November 2025

 Funding

This research work was funded by the Institutional Fund Projects under grant no. (IFPIP: 1446-415-1443). The author gratefully acknowledges the technical and financial support provided by the Ministry of Education and King Abdulaziz University, DSR, Jeddah, Saudi Arabia. 

 Acknowledgment

No Acknowledgment. 

 Compliance with ethical standards

 Ethical considerations

The datasets used in this study (PROSTATEx and the National Cancer Institute prostate MRI dataset) are publicly available and fully anonymized. All patient identifiers were removed by the data providers prior to release. All procedures were performed in accordance with relevant guidelines and regulations.

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Munshi RM (2025). An automated approach for prostate cancer detection using CGAN data augmentation with a weighted ensemble of transfer learning models. International Journal of Advanced and Applied Sciences, 12(12): 75-86

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 Figures

  Fig. 1  Fig. 2

 Tables

  Table 1  Table 2  Table 3  Table 4  Table 5  Table 6  Table 7  Table 8  Table 9 

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