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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- 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.
- Biomechanical and machine learning integration for the detection of knee injuries: exploring the utility of non-intrusive elements(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-08-15) Rosales Gurmendi, Diana Sofía Milagros; Muñoz Ubando, Luis Alberto; emimmayorquin; School of Engineering and Sciences; Campus MonterreyJoints play an essential role in the human body, allowing for movement, stability, and overall functionality. The knee, on the other hand, is a complex joint that contributes to the structural support of the body and enables a wide range of movements. Consequently, it is more susceptible to injury, which negatively impacts a person's daily routines. While there are important markers aiding in the assessment and diagnosis of joint health, they are typically evaluated using traditional qualitative methods. In this study, we focus on utilizing computational Machine Learning (ML) tools for recognizing patterns in injured joints. Specifically, three potential biomarkers were evaluated: joint sound, range of motion, and qualitative pain assessment. The purpose of this work is to explore the contribution of these three biomarkers in identifying knee injuries. A total of 22 participants, including both male and female individuals aged 23 and above, with and without a history of joint injury, were recorded and compared. A handheld device designed to measure the angular displacement and crackling sound of the joint was utilized. The tests consisted of 5 flexion and extension cycles, each comprising 4 repetitions. Signals were pre-processed, and 13 features were extracted for each frame. Using a binary classifier, dimensionality reduction methods, and signal processing by segmentation, the results showed an accuracy of 82\% in classifying the data with a precision of 83\%. The Machine Learning model successfully identified distinctive patterns between healthy and injured knees. Furthermore, the significant importance of considering the knee joint sound, presence of pain, and range of motion in clinical diagnostic evaluations to determine the presence or absence of a joint injury was highlighted.