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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- Improving deep neural networks to identify depression using neural architecture search(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06) Hernández Silva, Erick; Trejo Rodríguez, Luis Ángel; emipsanchez; Cantoral Ceballos, José Antonio; González Mendoza, Miguel; School of Engineering and Sciences; Campus Estado de México; Sosa Hernández, Víctor AdriánA Neural Architecture Search (NAS) framework utilizing Evolutionary Algorithms (EAs) and a regressor model is proposed to improve the classification performance of Deep Neural Net- works (DNNs) for the early detection of Major Depressive Disorder (MDD) from speech data represented by Mel-Spectrograms. The framework automates the design of neural network architectures by systematically exploring a well-defined search space that integrates convo- lutional layers, batch normalization, dropout, max pooling, and self-attention mechanisms, aiming to capture both spatial and temporal features inherent in vocal signals. By optimiz- ing for the F1-score, the framework addresses challenges related to data imbalance, ensuring robust generalization across both depressed and non-depressed samples. The proposed approach employs an integer-based encoding scheme to represent candi- date architectures, coupled with repair and validation processes that ensure all architectures meet specific design constraints. A self-adaptive mechanism dynamically adjusts the muta- tion factor based on evolutionary feedback, improving the balance between exploration and exploitation during the search process. Furthermore, a surrogate model, built using Princi- pal Component Analysis (PCA) and XGBoost regressor, predicts architecture performance, significantly reducing computational costs by avoiding full model training for all candidates. Experimental validation, conducted on publicly available speech datasets, demonstrates that NAS-generated architectures may outperform manually designed state-of-the-art models in terms of F1-score, accuracy, precision, recall, and specificity. The results confirm the effec- tiveness of integrating self-attention mechanisms with convolutional operations for extracting relevant depression-related vocal biomarkers. This research underlines the potential of NAS in advancing non-invasive, scalable, and interpretable AI-driven tools for mental health as- sessment, contributing to early intervention strategies and improving clinical outcomes in depression diagnosis.
- Multimodal neuroimaging and explainable deep learning for characterizing brain aging: insights into biomarkers of healthy and pathological aging(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-05) Cárdenas Castro, Héctor Manuel; Cantoral Ceballos, José Antonio; emipsanchez; Trejo Rodríguez, Luis Ángel; Castañeda Miranda, Alejandro; School of Engineering and Sciences; Campus Monterrey; Caraza Camacho, RicardoThe aging brain undergoes complex structural and functional transformations that differ- entiate healthy aging from pathological trajectories such as dementia. This study pioneers a multimodal neuroimaging and explainable deep learning framework to characterize brain aging, identify biomarkers of neurodegeneration, and elucidate the interplay between local anatomical changes and global network reorganization. Leveraging structural MRI-derived volumetrics and graph theory-based connectivity metrics extracted from resting-state fMRI from a heterogeneous cohort of cognitively healthy individuals and patients with Dementia attributed to Alzheimer’s and non-Alzheimer’s Disease, two predictive models were devel- oped: (1) a brain-age regression model to quantify deviations from normative aging patterns and (2) a dementia classification model to distinguish pathological from healthy aging. Both models achieved robust performance (mean absolute error = 0.68 years for controls in re- gression; F1-score = 0.93 for classification), with interpretable feature contributions revealed through SHAP (SHapley Additive exPlanations) analyses. Explainable AI (SHAP) analyses revealed non-linear feature interactions and highlighted established and novel neuroanatom- ical correlates of brain aging and dementia. By synthesizing computational innovation with clinical neuroimaging, this research provides actionable biomarkers for aging research, re- fines the conceptual framework of compensatory brain reorganization, and establishes a new contribution for AI-driven precision diagnostics in neurodegenerative disorders.
- Assesment of a modern convNet model in the detection of breast cancer in the mexican population(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-05) Monsivais Molina, Mario Alexis; Tamez Peña, Jose Gerardo; emimmayorquin; School of Engineering and Sciences; Campus MonterreyThis thesis presents an evaluation of a modern Convolutional Neural Network (ConvNet) model for detecting breast cancer in mammograms from the Mexican population. The study focused on implementing and testing a state-of-the-art ConvNet model, known as ConvNeXt, to assess its performance and reliability in diagnosing breast cancer. By employing the Tec-Salud dataset, which includes mammograms annotated by expert radiologists, and comparing it against the RSNA dataset, the research aimed to verify the model’s efficacy across different demographic and technological settings. The methodology involved preprocessing the images to standardize the data, followed by extensive training and validation of the ConvNeXt model. Performance metrics such as accuracy, sensitivity, specificity, and the area under the ROC curve were calculated to gauge the model's diagnostic power. Additionally, the study explored the impact of data augmentation and image normalization on model performance, emphasizing the challenges of applying AI in medical diagnostics across diverse populations. The findings revealed that while the ConvNeXt model demonstrated high accuracy and reliability, challenges such as overfitting and data bias persisted, highlighting the importance of continuous model training and validation. The study contributes significantly to the ongoing efforts in integrating AI into breast cancer diagnostics, offering insights into the potential of modern deep learning models to enhance early detection and treatment strategies in a demographically diverse patient population.