| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560665 |
| ISSN: | 1405-5546 |
| Autores: | Gaytán Campos, Israel1 Morales Castro, Wendy2 Priego Sánchez, Belem3 Fitz Rodríguez, Efrén1 Guzmán Cabrera, Rafael2 |
| Instituciones: | 1Universidad Autónoma Chapingo, Posgrado de Ingeniería Agrícola y Uso Integral del Agua, México 2Universidad de Guanajuato, Departamento de Estudios Multidisciplinarios, México 3Universidad Autónoma Metropolitana, División de Ciencias Básicas e Ingeniería, México |
| Año: | 2022 |
| Periodo: | Ene-Mar |
| Volumen: | 26 |
| Número: | 1 |
| Paginación: | 325-336 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | Skin cancer is the most frequent malignant neoplasm in the world, it is a public health problem, which has increased in recent years due to environmental changes, different lifestyles, sun exposure, among others. One way to detect skin cancer is through the analysis of medical images, the analysis of these images can allow the detection of any abnormality. In this work, several block programming models with classifiers for the recognition of medical images of skin cancer are implemented. Pre-processing, manipulation, and machine vision to extract relevant features from images are the starting point for obtaining proper classification values. The main objective of this project is to carry out the analysis of a set of classification techniques, as well as verify that the combination of image processing operations and classification tools provide better performance compared to the classification values of the original images. Images of three types of skin cancer were used: melanocytic nevus, melanoma, and benign keratosis lesions. Each category contains 200 images. The images were subjected to a set of filters, to later use different classification algorithms. Six types of filters and five different classification techniques were implemented. The results obtained allow us to see the viability of the proposed method. |
| Keyword: | Skin cancer, Image processing, Machine learning, Artificial intelligence |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
