IDENTIFICATION OF CELL MEMBRANES IN 2D IMAGES USING COMPUTER VISION

Paul D. Saravia-Velasquez, Roxana Flores-Quispe, Yuber Velazco-Paredes

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

Resumen

Today in many types of research the cellular structure has been studied to identify diseases or to do clinical diagnostics; of fungi and epithelial cells, because the increase in invasive fungal infections in recent years, especially in immunocompromised patients, has prompted the search for new antifungal agents with greater efficacy. Where the fungal cell membrane is enriched with various lipids belonging to the class of glycerophospholipids, sphingolipids, and sterols. In addition, the precise roles of membrane lipids in the organization of these membrane domains in epithelial cells, where they are polarized and maintain apical and basolateral membranes, are largely unknown. These epithelial cells have morphologically distinct membrane structures with specific functions. This research presents a significant contribution to achieving the precise identification of cell membranes in 2D images based on Inception-type CNN architecture specifically designed for this research context, allowing effective detection of cell membranes. In addition, Canny, Thresholding, and Gaussian noise filters were implemented to improve the edge detection extracting relevant information from the object of interest and reducing unnecessary elements in the image. In addition, other segmentation techniques and size adjustments were used to improve the variability present in the images. Finally, the experimental results show the best performance with an accuracy rate of 86.6% using the Adagrad optimizer, which proved its efficiency in the search for membranes in 2D images. Our proposal, based on CNN, image processing, and adjustment techniques, offers a robust approach to this task, strengthening research in this field and opening opportunities for future improvements.

Idioma originalInglés
Páginas (desde-hasta)5940-5951
Número de páginas12
PublicaciónJournal of Theoretical and Applied Information Technology
Volumen101
N.º19
EstadoPublicada - 15 oct. 2023

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