Browsing by Autor "Xavier Alexis Murillo Sanchez"

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A Portable Embedded System for Skin Lesions Detection
(2024) Giancarlo López Bustos; Xavier Alexis Murillo Sanchez; Edgar Eduardo Salazar Florez
Skin cancer is one of the most common types of cancer in the present decade. It can be classified into two categories: melanoma and non-melanoma. Melanoma is a dangerous, rare, and fatal type of skin cancer. Detection of melanoma in early stages is beneficial because it can improve the survival rate. As such in this paper, we developed an embedded system using a Jetson Nano that employs image processing techniques based on the ABCD rule to classify skin lesions according risk levels. The structure of the proposed system consists of two phases: the first phase involves an algorithm that performs segmentation, feature extraction, and classification; the second phase includes the electronic and mechanical design of the embedded system. This embedded system is intended to serve as a support tool for patients and doctors in the diagnosis of skin lesions and it represents the first of its kind in Bolivia.
An Algorithm for Medical Specialists to Assist in the Diagnosis of Down Syndrome Using Nasal Bone Location in Ultrasound Images
(2025) Giancarlo López-Bustos; Sergio Velásquez; Xavier Alexis Murillo Sanchez; Edgar Eduardo Salazar Florez
Down syndrome is a genetic disorder caused by the presence of an additional chromosome at position 21, also known as trisomy 21. This condition leads to intellectual disability and affects the physical and cognitive development of the child. For early diagnosis, medical specialists consider two key parameters: the absence or presence of the nasal bone and the size of the nuchal translucency. These assessments are typically conducted between 11–13 weeks of the 1<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> trimester of gestation using ultrasound imaging. In this paper we propose an algorithm to assist in the diagnosis of Down Syndrome by detecting the presence of nasal bone using a data-independent algorithm. The image processing pipeline consists of segmentation procedures developed grouping higher intensity pixels with similar features and detecting the nasal bone based on pattern recognition using geometrical features. The algorithm was tested with 45 images captured locally, 43 of which are normal cases and 2 are Down Syndrome obtaining an accuracy of 84% and the evaluation was carried out with an ultrasound specialist doctor.
Optimized Mammography Preprocessing for Tumor Detection with YOLO11-Seg in Young Women from the Bolivian Altiplano
(2025) Leoni Marti Miranda Saravia; Alejandro Rommel Miranda Saravia; Alicia Seminario Vargas; Marcelo Molina Silva; Yancarla Mary Conde Canaviri; Manuel Conde; Leonardo Lamas; Xavier Alexis Murillo Sanchez; M. Martín Sánchez
Early diagnosis of breast cancer in young women presents a critical clinical challenge, particularly in geographic contexts such as the Bolivian Altiplano, where high breast density and limited access to specialized technologies hinder detection. This study evaluates the impact of various image preprocessing techniques on the performance of an automatic detection model based on YOLO11-seg. Using a dataset of mammograms annotated by certified radiologists, transformations such as CLAHE, histogram equalization, Canny filtering, wavelets, and anisotropic diffusion were applied. Standard metrics (mAP, precision, recall) were measured and results were compared in a real clinical setting. Findings show that CLAHE significantly improves the model's ability to detect lesions in dense breasts, achieving a mAP of 71.8%. The results suggest that combining enhancement techniques with AI models can strengthen early detection in high-risk populations, offering a viable and scalable alternative for resource-limited settings.