Analysis of gastric cancer using artificial intelligence: An innovative approach in oncological research.
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Abstract
Gastric cancer (GC) remains one of the leading causes of cancer-related mortality worldwide, primarily due to the challenges of early diagnosis, which often relies on invasive procedures and manual image analysis. This study presents the development of a web-based application powered by artificial intelligence, using convolutional Neural Network for the automatic detection of anomalies in endoscopic images.
The methodology was structured into four phases: data collection, preprocessing, model implementation, and system evaluation. The algorithm achieved a classification accuracy of 97.4% and an average analysis time of 0.10 seconds per image, exceeding clinical performance benchmarks.
The system proved to be effective, fast, and accessible to healthcare professionals, including those in resource-constrained environments. Its user-friendly interface and centralized database facilitated seamless integration into clinical practice.
This research highlights the potential of deep neural networks in supporting early diagnosis of gastric cancer. Future work will focus on validating the tool in real-world clinical settings and exploring additional deep learning architectures to further enhance system performance.
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References
R. Jinushi, “Efficacy for diagnoses of scirrhous gastric cancer and safety of endoscopic ultrasound‐guided fine‐needle aspiration: A systematic review and meta‐analysis,” JGH Open, vol. 7, no. 6, pp. 403,.
M. Vega, L. Mora, and M. Badilla, “Inteligencia artificial y aprendizaje automático en medicina,” Revista MedicaSinergia, vol. 5, no. 8, pp. 557,.
M. Gonzáles, “Aplicación de la inteligencia artificial para la detección del cáncer de mama,” Revista Médica Sinergia, vol. 08, online]. Available:. [Online]. Available: https://revistamedicasinergia.com/index.php/rms/article/view/1113/2350
A. Vega and I. Vera, “Anatomía patológica, citodiagnóstico e inteligencia artificial: análisis de desafíos y beneficios,” Revista Ocronos.
P. Karimi, F. Islami, S. Anandasabapathy, N. Freedman, and F. Kamangar, “Gastric cancer: Descriptive epidemiology, risk factors, screening, and prevention,” Cancer Epidemiol Biomarkers Prev, vol. 23, no. 5, pp. 700,.
A. Cruz, “Generalidades del cáncer gástrico,” Revista médica de Costa Rica y Centroamérica, vol. 70, online]. Available:. [Online]. Available: https://www.medigraphic.com/cgi-bin/new/resumen.cgi?IDARTICULO=43998
F. Bray, “Global cancer statistics 2022: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA Cancer J Clin, vol. 74, no. 3, pp. 229–263,.
D. Olivera, “Diagnóstico de cáncer mediante inteligencia artificial,” Jun, vol. 10, online]. Available:. [Online]. Available: https://buleria.unileon.es/handle/10612/18360
G. Chacón, “Paradigmas de inteligencia artificial aplicados en imágenes para la detección de cáncer gástrico,” in La investigación social: comprendiendo fenómenos en contexto, U. Bolívar., A. Aguilar, and Y. Hernández, Eds., Barranquilla, p. 103–127.
M. Langarano, “Implementación de un sistema web para la interpretación, procesamiento y visualización de imágenes dicom en el hospital guayaquil,” online]. Available:. [Online]. Available: http://dspace.ups.edu.ec/handle/123456789/16507
A. John and D. Meva, “A comparative study of various object detection algorithms and performance analysis,” International Journal of Computer Sciences and Engineering Open Access Research Paper, vol. 8, no. 10.
J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,” Apr, online]. Available:. [Online]. Available: https://arxiv.org/pdf/1804.02767
A. Sharma, “Dermatologist-level classification of skin cancer using cascaded ensembling of convolutional neural network and handcrafted features based deep neural network,” IEEE Access, vol. 10, pp. 17 920–17 932,.
M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in 36th International Conference on Machine Learning, ICML, vol. 2019-June, pp. 10 691–10 700„ online]. Available:. [Online]. Available: https://arxiv.org/pdf/1905.11946
J. AlSamhori, “Artificial intelligence for breast cancer: Implications for diagnosis and management,” Journal of Medicine, Surgery, and Public Health, vol. 3, pp. 100 120,.
E. Criollo, “Comparativa de redes de aprendizaje profundo empleando transfer learning para detección de cáncer de mama en el corpus cbis-ddsm,” online]. Available:. [Online]. Available: http://dspace.ups.edu.ec/handle/123456789/28664
S. O. Ortega, “Aproximación al uso de interfaz gráfica en la operatividad de agentes inteligentes con imágenes médicas,” Accessed: Jun, vol. 38, pp. 109–115„ online]. Available:. [Online]. Available: http://saber.ucv.ve/ojs/index.php/rev_aavft/article/view/16449
J. Espitia, “Desarrollo de una aplicación móvil para el diagnóstico y tratamiento de la infección por h. pylori para profesionales de la salud,” Universidad Nacional de Colombia, Bogotá, accessed: Jun. 28, 2024. [Online]. Available:. [Online]. Available: https://repositorio.unal.edu.co/handle/unal/80648?show=full
J. Esteban, P. Arias, J. Sebastián, and R. Zapata, “Sistema web ia para detección y seguimiento del cáncer de mama,” Tecnológico de Antoquia, Medellín, accessed: Dec. 18, 2024. [Online]. Available:. [Online]. Available: https://repositorio.tdea.edu.co/handle/tdea/5638
H. Ding, “Segstrong-c: Segmenting surgical tools robustly on non-adversarial generated corruptions – an endovis’24 challenge,” Jul, online]. Available:. [Online]. Available: https://arxiv.org/abs/2407.11906v1
J. G. D. M. G. D. M. E. R. M. Anatomopatólogo, “Atlas de imÁgenes endoscÓpicas. fice.”
“El atlas gastrointestinal - gastrointestinalatlas.com,” Accessed: Jan, vol. 22, online]. Available:. [Online]. Available: https://www.gastrointestinalatlas.com/espanol/espanol.html