Ciencias Exactas y Ciencias de la Salud

Now showing 1 - 2 of 2

Face detection and feature extraction for classification tasks on thermal images of Covid-19 patients
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06) Ramírez Treviño, Luis Javier; Tamez Peña, José Gerardo; emimmayorquin; Santos Díaz, Alejandro; Martínez Ledesma, Juan Emmanuel; School of Engineering and Sciences; Campus Monterrey
This thesis presents a methodology for diagnosing COVID-19 patients using computer vision, infrared thermography, and machine learning. The study focuses on the analysis of thermal images, which offer a non-invasive and contactless alternative to traditional imaging methods like computed tomography (CT) and radiography. The research leverages a database of thermal images from 252 patients, including both COVID-19 positive and negative cases, to explore the potential of infrared thermography in detecting respiratory diseases. The proposed methodology involves two main approaches: one using a Convolutional Neural Network (CNN) to extract features from the full thermal image, and another incorporating a face detection step to focus on facial features. Three face detection algorithms—Haar Cascades, Local Binary Patterns (LBP), and CNNs (specifically YOLOv5)—were evaluated, with achieved accuracies of 93%, 98%, and 100%, respectively. Feature extraction was performed using the VGG-16 CNN architecture, pre-trained on the ImageNet dataset, followed by classification using traditional machine learning models such as Logistic Regression, AdaBoost, Support Vector Machines (SVM), Random Forest, and Gradient Boosting. The methodology was tested on two classification tasks: gender classification and COVID-19 symptom classification. For gender classification, the full-body approach achieved accuracies ranging from 0.933 to 0.996, while the face-only approach yielded slightly lower accuracies (0.868 to 0.923). For symptom classification, the full-body approach achieved accuracies between 0.607 and 0.650, outperforming previous work using radiomic features on the same dataset. The face-only approach for symptom classification resulted in accuracies ranging from 0.544 to 0.612, still demonstrating improvement over prior results. The study concludes that the proposed methodology is effective for classification tasks on thermal images, particularly for gender classification. While the results for symptom classification are not yet reliable enough for standalone diagnostic use, the high sensitivity scores suggest potential as a screening tool. The research highlights the promise of infrared thermography combined with machine learning for medical applications, especially in scenarios where traditional imaging methods are impractical or pose risks due to radiation exposure. Future work could explore data augmentation, additional patient data, and applications in other medical domains.
Automated radiology report generation using radiomics and natural language processing techniques
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-01) Bosques Palomo, Beatriz Alejandra; Tamez Peña, José Gerardo; emipsanchez; Santos Díaz, Alejandro; Avendaño Avalos, Daly Betzabeth; Helguera, Maria; Escuela de Ingeniería y Ciencias; Campus Monterrey
This thesis addresses the significant challenges in breast cancer diagnosis in developing countries, where delayed follow-ups due to resource constraints can impede timely and accurate detection, affecting patient outcomes. A novel approach using radiomic features integrated with transformer models to automate mammography report generation, specifically focusing on report conclusions is proposed. The primary goal is to assess if these AI-driven models can replicate the diagnostic accuracy of expert radiologists in assigning BI-RADS categories and recommending follow-ups or biopsies. The study begins with meticulous image preprocessing, including a customized histogram matching scheme to standardize input data and reduce variability among images from different vendors. Radiomic features were then extracted and validated through a classification task obtaining an AUC of 0.81, proving their efficacy as inputs for the transformer architecture. The transformer models utilized both radiomic features and deep learning features extracted via a pretrained CNN. This approach allowed for a direct comparison of model performance between the hand-crafted radiomic inputs and the more complex deep learning features against expert evaluations. Results showed that the models reached high agreement with radiologists’ evaluations, with kappa values reaching up to 0.93 for the simpler BI-RADS categorization task (1 & 5) using deep learning features. However, performance declined in more complex cases, with kappa values dropping to 0.23 for radiomic features across all BI-RADS categories (1, 2, 3, 4 & 5), indicating only fair agreement. In contrast, deep learning features maintained a moderate agreement with a kappa of 0.41. Despite these promising results, the study acknowledges certain limitations, including the inability to fine-tune feature extraction due to the hand-crafted nature of radiomic features, as well as the potential subjectivity in the data, given that radiologist evaluations are susceptible to human error. Nonetheless, this research lays crucial groundwork for future AI advancements in radiological diagnostics, aiming to enhance the efficiency, accuracy, and comprehensiveness of medical image analysis in resource-limited settings.

Ciencias Exactas y Ciencias de la Salud

Browse

Filters

Settings

Sort By

Results per page

Search Results