Irari Jiménez-López
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Irari Jiménez-López has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Jeniffer Molina
Cinvestav-IPN
2 shared publications - Juan Manuel Gutiérrez
Cinvestav-IPN
2 shared publications - José E. Valdez Rodríguez
Instituto Politécnico Nacional
1 shared publication - Luis Fernando Valdez
Cinvestav-IPN
1 shared publication - Marco Antonio Moreno-Armendariz
Centro de Investigación en Computación, Instituto Politécnico Nacional, Ciudad de México 07738, Mexico
1 shared publication
Latest Publications
- Open Access
Color Space Comparison of Isolated Cervix Cells for Morphology Classification
Cervical cytology processing involves the morphological analysis of cervical cells to detect abnormalities. In recent years, machine learning and deep learning algorithms have been explored to automate this process. This study investigates the use of color space transformations as a preprocessing technique to reorganize visual information and improve classification performance using isolated cell images. Twelve color space transformations were compared, including RGB, CMYK, HSV, Grayscale, CIELAB, YUV, the individual RGB channels, and combinations of these channels (RG, RB, and GB). Two classification strategies were employed: binary classification (normal vs. abnormal) and five-class classification. The SIPaKMeD dataset was used, with images resized to pixels via zero-padding. Data augmentation included random flipping and ±10° rotations applied with a 50% probability, followed by normalization. A custom CNN architecture was developed, comprising four convolutional layers followed by two fully connected layers and an output layer. The model achieved average precision, recall, and F1-score values of 91.39%, 91.34%, and 91.31% for the five-class case, respectively, and 99.69%, 96.68%, and 96.89% for the binary classification, respectively; these results were compared with a VGG-16 network. Furthermore, CMYK, HSV, and the RG channel combination consistently outperformed other color spaces, highlighting their potential to enhance classification accuracy.Open AccessColor Space Comparison of Isolated Cervix Cells for Morphology Classification
Cervical cytology processing involves the morphological analysis of cervical cells to detect abnormalities. In recent years, machine learning and deep learning algorithms have been explored to automate this process. This study investigates the use of color space transformations as a preprocessing technique to reorganize visual information and improve classification performance using isolated cell images. Twelve color space transformations were compared, including RGB, CMYK, HSV, Grayscale, CIELAB, YUV, the individual RGB channels, and combinations of these channels (RG, RB, and GB). Two classification strategies were employed: binary classification (normal vs. abnormal) and five-class classification. The SIPaKMeD dataset was used, with images resized to pixels via zero-padding. Data augmentation included random flipping and ±10° rotations applied with a 50% probability, followed by normalization. A custom CNN architecture was developed, comprising four convolutional layers followed by two fully connected layers and an output layer. The model achieved average precision, recall, and F1-score values of 91.39%, 91.34%, and 91.31% for the five-class case, respectively, and 99.69%, 96.68%, and 96.89% for the binary classification, respectively; these results were compared with a VGG-16 network. Furthermore, CMYK, HSV, and the RG channel combination consistently outperformed other color spaces, highlighting their potential to enhance classification accuracy. - 6 May 2025
- Published by Springer Nature
- p. 50-57
Classification of Teas Using Machine and Deep Learning Methods on a Custom e-nose Platform
- 6 May 2025
- Published by Springer Nature
- p. 50-57
Classification of Teas Using Machine and Deep Learning Methods on a Custom e-nose Platform
- Open Access
- 12 October 2023
- Published by MDPI AG in Engineering Proceedings
- Vol. 48 (1) , 20
Classification of Teas Using Different Feature Extraction Methods from Signals of a Lab-Made Electronic Nose
Tea and herbal infusions are the most consumed non-alcoholic beverages worldwide and possess bioactive components with multiple health benefits. They are categorized into different classes that depend on their elaboration process, origin, and components. Commonly, analytical methods are employed to classify tea according to its chemical composition using liquid and gas chromatography–mass spectrometry, among others. Novel methods, such as electronic noses (e-noses), effectively provide real-time and objective monitoring of odors for extended periods of time. This work aimed to classify eight different types of tea (green, white, black, spearmint, mint, hibiscus, lemongrass, and chamomile) using two feature extraction methods and two pattern recognition analyses that were compared. A total of 34 tea samples were analyzed using an e-nose consisting of an olfactometer as a sample-handling system, seven chemo-resistive gas sensors, and a 12-bit analog-to-digital converter. Tea samples were measured 10 times to ensure repeatability, resulting in a database of 340 tea measures with 2499 samples each per sensor. Data were preprocessed using Principal Component Analysis (PCA) and Parallel Factor Analysis (PARAFAC). The information extracted was classified using an Artificial Neural Network (ANN) and k-nearest neighbor (k–NN). The best architecture in ANN and distance in k-NN were demonstrated through 10 k-fold cross-validation. The classification rate was 93% in ANN and PCA, 73% in ANN and PARAFAC, 94% in k-NN and PCA, and 84% in k-NN and PARAFAC. This demonstrates that conventional PCA is better than complex PARAFAC. Our findings not only contribute to the field of tea and herbal infusion classification but also underscore the potential of e-nose systems for discriminating between diverse tea types and herbal infusions based on their odor profiles.
- 12 October 2023
- Published by MDPI AG in Engineering Proceedings
- Vol. 48 (1) , 20
Open AccessClassification of Teas Using Different Feature Extraction Methods from Signals of a Lab-Made Electronic Nose
Tea and herbal infusions are the most consumed non-alcoholic beverages worldwide and possess bioactive components with multiple health benefits. They are categorized into different classes that depend on their elaboration process, origin, and components. Commonly, analytical methods are employed to classify tea according to its chemical composition using liquid and gas chromatography–mass spectrometry, among others. Novel methods, such as electronic noses (e-noses), effectively provide real-time and objective monitoring of odors for extended periods of time. This work aimed to classify eight different types of tea (green, white, black, spearmint, mint, hibiscus, lemongrass, and chamomile) using two feature extraction methods and two pattern recognition analyses that were compared. A total of 34 tea samples were analyzed using an e-nose consisting of an olfactometer as a sample-handling system, seven chemo-resistive gas sensors, and a 12-bit analog-to-digital converter. Tea samples were measured 10 times to ensure repeatability, resulting in a database of 340 tea measures with 2499 samples each per sensor. Data were preprocessed using Principal Component Analysis (PCA) and Parallel Factor Analysis (PARAFAC). The information extracted was classified using an Artificial Neural Network (ANN) and k-nearest neighbor (k–NN). The best architecture in ANN and distance in k-NN were demonstrated through 10 k-fold cross-validation. The classification rate was 93% in ANN and PCA, 73% in ANN and PARAFAC, 94% in k-NN and PCA, and 84% in k-NN and PARAFAC. This demonstrates that conventional PCA is better than complex PARAFAC. Our findings not only contribute to the field of tea and herbal infusion classification but also underscore the potential of e-nose systems for discriminating between diverse tea types and herbal infusions based on their odor profiles.