Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Identification of species of plants of the Solanum (Solanaceae) genus native to Mexico using computational vision and convolutional neural networks on pictures of herbarium specimens(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-02) Hernández Rincón, Carlos Eduardo; Falcón Morales, Luis Eduardo; emipsanchez; Rodríguez Contreras, Aarón; Mendoza Montoya, Omar; Escuela de Ingeniería y Ciencias EIC; Campus GuadalajaraThe development of Deep Learning techniques like Convolutional Neural Networks for automated image processing has been making big strides in recent years. This has helped to find more practical applications in many science fields. One such field is that of botanic taxonomic analysis which aims to accurately identify and classify new species of plants. It is important not only for scientific purposes but also for taking appropriate conservation actions, for economic reasons and for proper environment policy making. However, doing this requires a lot of technical skills and time and the number of qualified people at herbaria and scientific institutions in Mexico is not enough. Moreover, a significant number of new plant species have already been collected but are sitting unidentified in herbaria across the country. The Solanum genus encompasses species such as potatoes, eggplants and tomatoes. It is one of the most diverse and important for its economic, nutritious and cultural value worldwide. Mexico is no exception, and it is home to many species both discovered and undiscovered. Currently there is a project at Universidad de Guadalajara to identify all species of the Solanum genus native to Mexico that have already been collected at different herbaria. Convolutional Neural Networks could help with this huge task. The main purpose of this research is to prove that a system to assist a human taxonomist identify these plants is feasible and indeed helpful.
- A deep-learning application for epithelial cells image detection(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-09-16) Anaya Alvarez, Sergio Eduardo; CORTES CAPETILLO, AZAEL JESUS; 366841; Cortes Capetillo, Azael Jesus; tolmquevedo/mscuervo; Güemes Castorena, David; Lozoya Santos, Jorge de Jesús; School of Engineering and Sciences; Campus MonterreyUrinary particles are used to evaluate the different urinary tract diseases in patients. Currently, doctors use the traditional methods for urinalysis such as urine dipstick, urine culture and microscopy. Microscopy is an effective method for the diagnosis and treatment of many kidney and urinary tract diseases. However, manual microscopic examination of urine is labor-intensive, subjective, imprecise, and time-consuming. In this project, we proposed the development of a different deep learning models classifier for an automated microscopic urinalysis system for epithelial cells. A dataset was constructed from scratch taking urine samples from the Hospital Ginequito obtaining a total of 857 images. Then, the images were labeled into urine samples with and without epithelial cells for binary classification. Last, we created three deep learning models using the InceptionV3 architectures with different series of fully connected layers randomly initialized and ReLU activation, a dropout rate of 0.2 and a final sigmoid layer for classification. The best model obtained a training accuracy of 81.89% with sensitivity of 77.84%, specificity of 85.94% and precision of 84.70% and a validation accuracy of 84.28% with a sensitivity of 87.50%, specificity of 81.25% and precision of 82.35%. It was concluded that microscopic urinalysis can be done automatically, this opens the door for the classification of more urine particles with improved metrics.