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Journal of Information and Optimization Sciences cover

Journal of Information and Optimization Sciences

(Since 1980)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Journal of Information and Optimization Sciences
Open Access Research Article

Deep ensemble learning model for cervical cancer disease classification on image dataset

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pp. 263–272Vol. 46Issue 1January 2025DOI: 10.47974/JIOS-1943XML
Received:
14 Aug 2024
Published Online:
19 Feb 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1943
Pages:
263–272

Abstract

Early detection is the hallmark of ensuring better patient outcomes and successful treatment for most forms of cancer. In our proposed model, we classify the cervical cancer disorders through the developed deep ensemble learning model that was trained on a large dataset of colposcopy photos. It is evident that combining deep learning with an ensemble methodology would make colposcopy data-driven cervical cancer screening more accurate and robust. The proposed model was experimented with various known performance measures such as sensitivity, specificity, accuracy, PPV, and NPV. The experimental outcome results find this ensemble deep learning model to perform outstandingly well with remarkable robustness and outstanding accuracy in ‘detecting’ cervical cancer over individual models. Integrating the Colposcopy Ensemble Network architecture developed to address the problem of cervical cancer detection, this model increases the overall sensitivity and accuracy. Thus, it is highly efficient given other performance standards and, what is even more important, sets the scene for investigating and fostering the development of ensemble learning models in classifying various diseases based on medical images

Keywords

EnsembleAccuracyDatasetsMedicalModelLearningDisease

Subject Classifications

68Txx68Uxx

References

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