Guevara Maldonado, César Byron
Thumbnail Image
Preferred name
Guevara Maldonado, César Byron
Main Affiliation
Quito
Email
cesarguevara@uti.edu.ec
ORCID
Scopus Author ID
56452706300

Research Output

Filters

4
4
Reset filters

Settings
Now showing 1 - 4 of 4
No Thumbnail Available
Publication

Development of a Convolutional Neural Network for Detection of Ovarian Cancer Based on Computed Tomography Images

2024 , Gabriela Narvaez-Chunillo , Ronny Ordoñez-Sanchez , Lizbeth Ortiz-Vinueza , Diego Almeida-Galárraga , Fernando Villalba-Meneses , Roberto Bravo-Freire , Andrés Tirado-Espín , Carolina Cadena-Morejón , Paulina Vizcaíno-Imacaña , Guevara Maldonado, César Byron

Ovarian cancer is one of the most frequent gynecologic malignancies in women, but it is often detected in late stage, leaving patients with little time to follow a successful therapy. Specialists have opted to use computer-aided diagnosis (CAD) for the detection of ovarian cancer through the analysis of computed tomography (CT) images, in which the professional examines the size, shape and different characteristics that enable a precise diagnosis in the ovary. This present project purposes a Convolutional Neural Network (CNN) which consist on four convolutional layers; including two pooling layer and two fully-connected layer. The cancerous ovaries images is selected from the Cancer Imaging Achive dataset for training and validation of the model. Moreover, the training of the CNN contain filters to ensure that all of the images are the same dimensions and pixel size. The testing results from the training of the images showed that the proposed model obtained a range of accuracy that goes from 90.0% to the best of the cases 98.85%. The variables obtained like the data of the pressure and loss of the training were compared with those of the validation, allowing for the determination of a successful CNN training.

View
No Thumbnail Available
Publication

Using Artificial Intelligence and X-ray Images to Train and Predict COVID-19 and Pneumonia: Tool for Diagnosis and Treatment

2025 , Bryan Juárez-Gonzalez , Fernando Villalba-Meneses , Jonathan Cruz-Varela , Andrés Tirado-Espín , Paulina Vizcaino-Imacaña , Carolina Cadena-Morejon , Guevara Maldonado, César Byron , Diego Almeida-Galárraga

In January 2022, Ecuador experienced a peak in COVID-19 cases, with 890,541 confirmed cases and 35,658 deaths. During 2019-2020, influenza and pneumonia also ranked among the top causes of death. Traditional chest X-rays and chest CT scans are commonly used for diagnosing COVID-19, but studies by Wong et al. indicate that chest X-rays are less sensitive compared to CT scans unless artificial intelligence is utilized. Tahir et al. highlighted that AI models such as U-net and neural networks achieve high diagnostic accuracy, with U-Net++ and ResNet18 models showing sensitivities above 99% and perfect specificity using a large dataset of 33,920 chest X-ray images. The rapid detection of symptoms could have helped in prioritizing critical care, potentially reducing deaths. In the current study, it is demonstrated that combining AI with chest X-rays can achieve a binary accuracy above 98% for COVID-19 detection using transfer learning with Xception, VGG16, and VGG19 neural networks. Using 27,052 chest X-ray images, the VGG19 model achieved an excellent F1-score of 98.53% for COVID-19 and normal class classification. The VGG19 model also performed well in multiclass classification with an F1-score above 89%. The study concludes that AI-enhanced diagnostic tools, such as VGG19, are valuable for hospitals in diagnosing COVID-19 and future improvements might include larger datasets and enhanced segmentation techniques.

View
No Thumbnail Available
Publication

Comparative Analysis of Neural Networks and Data Processing Techniques for Parkinson’s Gait Classification

2024 , Israel Reyes , Francis Andaluz , Kerly Troya , Luis Zhinin-Vera , Diego Almeida-Galárraga , Carolina Cadena-Morejón , Andrés Tirado-Espín , Santiago Villalba-Meneses , Guevara Maldonado, César Byron

Parkinson’s disease (PD) is an advancing neurodegenerative condition characterized by motor symptoms, including disturbances in gait and varying degrees of severity, typically assessed using the Hoehn and Yahr stages. Precise classification of PD gait patterns and severity levels is of paramount importance for efficient diagnosis and continuous treatment monitoring. This research article presents a comprehensive assessment of the performance of three distinct Artificial Neural Network (ANN) models integrated with diverse data processing techniques, encompassing segmentation, filtration, and noise reduction, in the context of classifying PD severity. The classification is based on the vertical ground reaction force (VGRF) measurements obtained from both healthy individuals and those afflicted by Parkinson’s disease, sourced from a well-established database (GaitPDB, Physio Net). The study provides a comparative analysis of the efficacy of these models in accurately discriminating between various gait patterns and stages of disease severity, underscoring their potential to enhance clinical decision-making and patient care. Additionally, the study offers valuable insights into the impact of data processing methodologies on classification performance

View
No Thumbnail Available
Publication

Topological Analysis Techniques for Improving Neural Network Performance in COVID-19 Detection Using Persistent Homology

2024 , Israel Reyes , Karen Cáceres-Benítez , Ana Marcillo , Andre Vera , Carolina Cadena-Morejón , Fernando Villalba-Meneses , Guevara Maldonado, César Byron , Paulina Vizcaíno-Imacaña , Diego Almeida-Galárraga , Andrés Tirado-Espín

In this study, we employ topological data analysis techniques on neural networks applied in COVID-19 detection, aiming to improve their predictive power. Leveraging the power of persistent homology, a mathematical tool for extracting topological features from intricate datasets, we turned chest X-ray images into a representation of the topological features. This representation was used to train and test the ability of neural networks to learn topological properties from images. We examine neural networks trained on chest radiographs containing both COVID-19 positive and negative cases. Our results suggest that, by identifying specific topological features correlated with COVID-19 detection, we may enhance the performance of the neural network models and analyze the underlying factors contributing to high accuracy rate of detection. The findings from this study contribute as exploratory advance in the field of medical imaging analysis and disease detection, showcasing the potential of topological analysis within neural networks.

View