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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- PassID: A Modular System for Pass Detection with Integrated Player Identification in Football(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Gutiérrez Padilla, Benjamín; Monroy Borja, Raúl; emimmayorquin; Gutiérrez Rodríguez, Andrés Eduardo; School of Engineering and Sciences; Campus Monterrey; Conant Pablos, Santiago EnriqueThe analysis of football passes plays a crucial role in understanding team tactics and improving performance. However, current methods for capturing and analyzing this data are often inaccessible due to high costs and reliance on proprietary datasets. This thesis presents the development of an automated system designed to detect passes in football matches using video as the source of information. The system integrates computer vision and machine learning techniques across mul tiple modules, including player and ball detection, object tracking, team identification, and pass detection. Using a hybrid approach with YOLOv9 for player detection, FasterRCNN for the ball, and Norfair for tracking, the system assigns unique identifiers to players and determines passes based on proximity and ball possession changes. Team identification is achieved through color histogram analysis, allowing the system to distinguish valid passes between players of the same team. The modular design enables independent improvements in each component, providing a flexible framework that can be adapted to different match conditions. This work represents a step forward in automating football pass detection, contributing to the growing field of sports analysis with a scalable and efficient solution.
- Evaluating Pre-trained Neural Networks in Deep Learning for Early Detection and Enhanced Screening of Cervical Pathology(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) González Ortiz, Orlando; Muñoz Ubando, Luis Alberto; emimmayorquin; Raymundo Avilés, Arturo; Cerón López Universidad, Arturo Eduardo; School of Engineering and Sciences; Campus Monterrey; Ochoa Ruiz, GilbertoThis document presents a research thesis for the Master in Computer Science (MCC) degree at Tecnologico de Monterrey. Cervical cancer remains a leading cause of mortality among women, particularly in low-resource regions where screening tools such as the Pap smear often fall short in early detection. This research explores the application of deep learning and pre-trained neural networks for the binary classification of cervical pathology, focusing on detecting dysplasia, specifically CIN2 and CIN3, as a potential prevention tool. We im- plemented multiple neural network models, including DenseNet, EfficientNet, MobileNet, and ResNet. The models were evaluated on two distinct datasets: one from the International Agency for Research on Cancer (IARC) and another from the Zambrano Hospital. To as- sess the generalization capacity of these models, we employed a sequential training approach where the first batch was trained with IARC data and tested on a Zambrano Hospital batch, with subsequent tests progressively incorporating prior results. Each experiment was repeated over 10 iterations to calculate confidence intervals for the performance metrics. Our results demonstrate that DenseNet and EfficientNet outperformed other models, achieving superior sensitivity and accuracy compared to conventional Pap smear tests. These findings indicate that deep learning models hold promise as an affordable, effective cervical cancer screening tool in low-resource communities. Future work will focus on augmenting datasets through collaboration with healthcare institutions and exploring generative models such as GANs to improve model robustness and generalization.
- Reinforcement learning for an attitude control algorithm for racing quadcopters(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-06-15) Nakasone Nakamurakari, Shun Mauricio; BUSTAMANTE BELLO, MARTIN ROGELIO; 58810; Bustamante Bello, Martín Rogelio; puemcuervo; Navarro Durán, David; School of Engineering and Sciences; Campus Ciudad de México; Galuzzi Aguilera, RenatoFrom its first conception to its wide commercial distribution, Unmanned Aerial Vehicle (UAV)’s have always presented an interesting control problem as their dynamics are not as simple to model and present a non-linear behavior. These vehicles have improved as the technology in these devices has been developed reaching commercial and leisure use in everyday life. Out of the many applications for these vehicles, one that has been rising in popularity is drone racing. As technology improves, racing quadcopters have also improved reaching capabilities never seen before in flying vehicles. Though hardware and performance have improved throughout the drone racing industry, something that has been lacking, in a way, is better and more robust control algorithms. In this thesis, a new control strategy based on Reinforcment Learning (RL) is presented in order to achieve better performance in attitude control for racing quadcopters. For this process, two different plants were developed to fulfill, a) the training process needs with a simplified dynamics model and b) a higher fidelity Multibody model to validate the resulting controller. By using Proximal Policy Optimization (PPO), the agent is trained via a reward function and interaction with the environment. This dissertation presents a different approach on how to determine a reward function such that the agent trained learns in a more effective and faster way. The control algorithm obtained from the training process is simulated and tested against the most common attitude control algorithm used in drone races (Proportional Integral Derivative (PID) control), as well as its ability to reject noise in the state signals and external disturbances from the environment. Results from agents trained with and without these disturbances are also presented. The resulting control policies were comparable to the PID controller and even outperformed this control strategy in noise rejection and robustness to external disturbances.
- COVID-19 mortality prediction using deep neural networks(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-06) García Zendejas, Arturo; MORALES MENENDEZ, RUBEN; 30452; REPOSITORIO NACIONAL CONACYT; Morales Menéndez, Rubén; emipsanchez; School of Engineering and Sciences; Campus MonterreyCOVID - 19 disease caused by the virus SARS-CoV2 appeared in Wuhan China in 2019, in March 11th 2020 it was declared a global pandemics, taking by March 2022 over 5,783,700 lives around the world. COVID-19 spreads in several different ways, the virus SARS-CoV2 which causes COVID-19 can spread from a mouth or nose of a person who is infected through liquid particles whenever they cough, sneeze, speak or breath. Initial symptoms and development of the illness are catalogued as mild, because of that it may be difficult to identify which persons will more probably develop severe disease. One great support that can be given to medical centers and healthcare workforce would be the ability to predict which patients will have a greater risk of death and would develop more quickly and severe illness, in order to make triage for treatment and decisions about resources distribution. Machine learning and specifically Deep Learning works by modelling hierarchical representations behind data, aiming to classify or predict patterns by stacking multiple layers of information. Some of its main applications are speech recognition, natural language processing, audio recognition, autonomous vehicles and even medicine. In medicine, it has been used to predict how a disease develops and affects patients. During this thesis it was done a research and comparison of state of the art articles and models that aim to predict the behavior and development of COVID-19 patients and the illness itself. Their different datasets, metrics, models and results have been studied and used as a base in order to create the proposed models of the thesis. This research project proposes the use of machine learning models to predict the mortality of COVID-19 patients by using as input attributes of the patients such as vital signs, biomarkers, comorbidities and diagnostics. This data was obtained for training and testing purposes from different medical centers, such as HM Hospitals, San Jose Hospital and CEM Hospital. The main Deep Learning model used during this thesis is a Deep Multi-layer Perceptron Neural Network which uses static attributes, and a Long-Short Term Memory Recurrent Neural Network using dynamic attributes. A mixed model combining the static and dynamic model was also created. It was also used metrics that support the reduction of false negative cases, the Maximum Probability of Correct Decision is the main metric to evaluate and optimize the model. The models have been evaluated and compared with another machine learning models such as Random Forest and eXtreme Gradient Boosting over the different datasets.
- Characterization of jet fire flame temperature zones using a deep learning-based segmentation approach(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-12-02) Pérez Guerrero, Carmina; OCHOA RUIZ, GILBERTO; 352103; Ochoa Ruiz, Gilberto; puemcuervo; González Mendoza, Miguel; Mata Miquel, Christian; School of Engineering and Sciences; Campus Monterrey; Palacios Rosas, AdrianaJet fires are relatively small and have the least severe effects among the diverse fire accidents that can occur in industrial plants; however, they are usually involved in a process known as domino effect, that leads to more severe events, such as explosions or the initiation of another fire, making the analysis of such fires an important part of risk analysis. One such analysis would be the segmentation of different radiation zones within the flame, therefore this thesis presents an exploratory research regarding several traditional computer vision and Deep Learning segmentation approaches to solve this specific problem. A data set of propane jet fires is used to train and evaluate the different approaches. Different metrics are correlated to a manual ranking performed by experts to make an evaluation that closely resembles the expert’s criteria. Additionally, given the difference in the distribution of the zones and background of the images, different loss functions, that seek to alleviate data imbalance, are explored. The Hausdorff Distance and Adjusted Rand Index were the metrics with the highest correlation and the best results were obtained from training with a Weighted Cross-Entropy Loss. The best performing models were found to be the UNet architecture, along with its recent variations, Attention UNet and UNet++. These models are then used to segment a group of vertical jet flames of varying pipe outlet diameters to extract their main geometrical characteristics. Attention UNet obtained the best general performance in the approximation of both height and area of the flames, while also showing a statistically significant difference between it and UNet++. UNet obtained the best overall performance for the approximation of the lift-off distances; however, there is not enough data to prove a statistically significant difference between UNet and its two variations. The only instance where UNet++ outperformed the other models, was while obtaining the lift-off distances of the jet flames with 0.01275 m pipe outlet diameter. In general, the explored models show good agreement between the experimental and predicted values for relatively large turbulent propane jet flames, released in sonic and subsonic regimes; thus, making these radiation zones segmentation models, a suitable approach for different jet flame risk management scenarios.
- 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.
- ANOSCAR: An image captioning model and dataset designed from OSCAR and the video dataset of activitynet(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-07-01) Byrd Suárez, Emmanuel; GONZALEZ MENDOZA, MIGUEL; 123361; González Mendoza, Miguel; puemcuervo; Ochoa Ruiz, Gilberto; Marín Hernandez, Antonio; School of Engineering and Sciences; Campus Estado de México; Chang Fernández, LeonardoActivity Recognition and Classification in video sequences is an area of research that has received attention recently. However, video processing is computationally expensive, and its advances have not been as extraordinary compared to those of Image Captioning. This work uses a computationally limited environment and learns an Image Captioning transformation of the ActivityNet-Captions Video Dataset that can be used for either Video Captioning or Video Storytelling. Different Data Augmentation techniques for Natural Language Processing are explored and applied to the generated dataset in an effort to increase its validation scores. Our proposal includes an Image Captioning dataset obtained from ActivityNet with its features generated by Bottom-Up attention and a model to predict its captions, generated with OSCAR. Our captioning scores are slightly better than those of S2VT, but with a much simpler pipeline, showing a starting point for future research using our approach, which can be used for either Video Captioning or Video Storytelling. Finally, we propose different lines of research to how this work can be further expanded and improved.
