Real-time cervical cancer risk assessment via mobile colposcopy and AI integration

Darwin Castillo Malla; Yuliana Jiménez Gaona; Bernardo José Vega Crespo; Veronique Verhoeven

doi:10.1117/12.3065644

Real-time cervical cancer risk assessment via mobile colposcopy and AI integration

Darwin Castillo Malla (Primer Autor)
, Yuliana Jiménez Gaona
, Bernardo José Vega Crespo
, Veronique Verhoeven (Último Autor)

Universidad de Cuenca

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

Resumen

Cervical cancer is one of the most common and dangerous cancers in women, especially in countries with limited resources. In this context, the principal aim of this project is to develop a tool that allows clinicians to screen for cervical cancer through the screening and processing of colposcopy images captured with a smartphone-based colposcope, Cervix app. The mobile application for cervical cancer diagnosis was developed using React Native and Firebase, enabling compatibility with iOS and Android devices. The application features a user-friendly and intuitive interface that facilitates the capture and analysis of colposcopy images. The classification and processing of images (benign and malignant) were conducted using the UNET model for segmentation, GANs for data augmentation, and ResNet models for classification. Several tests were conducted to evaluate the performance of the mobile application to predict and diagnose, ensuring its functionality was accurate and reliable at 90%.

Idioma original	Inglés
Título de la publicación alojada	Applications of Digital Image Processing XLVIII
Editores	Andrew G. Tescher, Touradj Ebrahimi
Lugar de publicación	San Diego, California
Editorial	SPIE
Páginas	1-9
Número de páginas	9
Volumen	13605
ISBN (versión digital)	9781510691186
ISBN (versión impresa)	9781510691186
DOI	https://doi.org/10.1117/12.3065644
Estado	Publicada - 17 sep. 2025
Evento	SPIE Optical Enginneering + Applications - San Diego, Estados Unidos Duración: 3 ago. 2025 → 8 ago. 2025

Serie de la publicación

Nombre	Proceedings of SPIE - The International Society for Optical Engineering
Volumen	13605
ISSN (versión impresa)	0277-786X
ISSN (versión digital)	1996-756X

Conferencia

Conferencia	SPIE Optical Enginneering + Applications
Título abreviado	SPIE
País/Territorio	Estados Unidos
Ciudad	San Diego
Período	3/08/25 → 8/08/25

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 3: Salud y bienestar

Palabras clave

Cervical cancer
Mobile application
Deep learning

Acceder al documento

10.1117/12.3065644

Otros archivos y enlaces

Citar esto

Castillo Malla, D., Jiménez Gaona, Y., Vega Crespo, B. J., & Verhoeven, V. (2025). Real-time cervical cancer risk assessment via mobile colposcopy and AI integration. En A. G. Tescher, & T. Ebrahimi (Eds.), Applications of Digital Image Processing XLVIII (Vol. 13605, pp. 1-9). Artículo 1360506 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 13605). SPIE. https://doi.org/10.1117/12.3065644

Castillo Malla, Darwin ; Jiménez Gaona, Yuliana ; Vega Crespo, Bernardo José et al. / Real-time cervical cancer risk assessment via mobile colposcopy and AI integration. Applications of Digital Image Processing XLVIII. editor / Andrew G. Tescher ; Touradj Ebrahimi. Vol. 13605 San Diego, California : SPIE, 2025. pp. 1-9 (Proceedings of SPIE - The International Society for Optical Engineering).

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title = "Real-time cervical cancer risk assessment via mobile colposcopy and AI integration",

abstract = "Cervical cancer is one of the most common and dangerous cancers in women, especially in countries with limited resources. In this context, the principal aim of this project is to develop a tool that allows clinicians to screen for cervical cancer through the screening and processing of colposcopy images captured with a smartphone-based colposcope, Cervix app. The mobile application for cervical cancer diagnosis was developed using React Native and Firebase, enabling compatibility with iOS and Android devices. The application features a user-friendly and intuitive interface that facilitates the capture and analysis of colposcopy images. The classification and processing of images (benign and malignant) were conducted using the UNET model for segmentation, GANs for data augmentation, and ResNet models for classification. Several tests were conducted to evaluate the performance of the mobile application to predict and diagnose, ensuring its functionality was accurate and reliable at 90\%. ",

keywords = "Cervical cancer, Mobile application, Deep learning, cervical cancer, cervix, deep learning, early detection, healthcare technology, medical image processing, mobile application",

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month = sep,

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Castillo Malla, D, Jiménez Gaona, Y, Vega Crespo, BJ & Verhoeven, V 2025, Real-time cervical cancer risk assessment via mobile colposcopy and AI integration. En AG Tescher & T Ebrahimi (eds.), Applications of Digital Image Processing XLVIII. vol. 13605, 1360506, Proceedings of SPIE - The International Society for Optical Engineering, vol. 13605, SPIE, San Diego, California, pp. 1-9, SPIE Optical Enginneering + Applications, San Diego, California, Estados Unidos, 3/08/25. https://doi.org/10.1117/12.3065644

Real-time cervical cancer risk assessment via mobile colposcopy and AI integration. / Castillo Malla, Darwin (Primer Autor); Jiménez Gaona, Yuliana; Vega Crespo, Bernardo José et al.
Applications of Digital Image Processing XLVIII. ed. / Andrew G. Tescher; Touradj Ebrahimi. Vol. 13605 San Diego, California: SPIE, 2025. p. 1-9 1360506 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 13605).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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KW - cervix

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Castillo Malla D, Jiménez Gaona Y, Vega Crespo BJ, Verhoeven V. Real-time cervical cancer risk assessment via mobile colposcopy and AI integration. En Tescher AG, Ebrahimi T, editores, Applications of Digital Image Processing XLVIII. Vol. 13605. San Diego, California: SPIE. 2025. p. 1-9. 1360506. (Proceedings of SPIE - The International Society for Optical Engineering). doi: 10.1117/12.3065644