Daniella Lúmara Peres
Daniella Lúmara Peres has a total of 13 co-authors. Here are the top co-authors by number of shared publications:
- Denise Maria Zezell
Center for Lasers and Applications, Instituto de Pesquisas Energéticas e Nucleares, São Paulo, Brazil
4 shared publications - Daniela Fátima Teixeira Silva
Center for Lasers and Applications, Instituto de Pesquisas Energéticas e Nucleares, São Paulo, Brazil
3 shared publications - Joaquim Cezar Felipe
Sciences and Letters at Ribeiro Preto, University of So Paulo,Faculty of Philosophy,Ribeiro Preto,Brazil
3 shared publications - Leandro Luongo Matos
Head and Neck Surgery, Instituto do Câncer do Estado de São Paulo, Hospital das Clínicas, Universidade de São Paulo (Icesp HCFMUSP). Professor, Surgery, Faculdade Israelita de Ciências da Saúde Albert Einstein, São Paulo, Brazil
3 shared publications - Thiago Martini Pereira
Institute of Science and Technology (ICT‐UNIFESP) Federal University of São Paulo São José dos Campos São Paulo Brazil
3 shared publications - Luciana Corrêa
General Pathology Department, School of Dentistry, University of São Paulo, Sao Paulo, Brazil
2 shared publications
Latest Publications
- 21 September 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-3
Impact of Sampling Strategies on the Classification of Micro-FTIR Hyperspectral Data
This study evaluated class balancing strategies for classifying oral squamous cell carcinoma (OSCC) in FTIR hyperspectral images using the XGBoost model. Although the dataset was balanced at the image level, spectral quality filtering introduced pixel-level class imbalance. Resampling methods—SMOTE, Tomek Links, and their combination—were tested, as well as AllKNN for redundancy reduction. All approaches outperformed the unbalanced baseline, but the best overall performance metrics were achieved with the combined use of AllKNN and Tomek Links.
- 21 September 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-3
Impact of Sampling Strategies on the Classification of Micro-FTIR Hyperspectral Data
This study evaluated class balancing strategies for classifying oral squamous cell carcinoma (OSCC) in FTIR hyperspectral images using the XGBoost model. Although the dataset was balanced at the image level, spectral quality filtering introduced pixel-level class imbalance. Resampling methods—SMOTE, Tomek Links, and their combination—were tested, as well as AllKNN for redundancy reduction. All approaches outperformed the unbalanced baseline, but the best overall performance metrics were achieved with the combined use of AllKNN and Tomek Links.
- 21 September 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-3
Discriminating Oral Squamous Cell Carcinoma via $\mu$ FTIR Spectroscopy Imaging and Tree-Based ModelsThis study evaluates the classification of oral squamous cell carcinoma (OSCC) using micro-FTIR hyperspectral imaging and two tree-based models: Random Forest and XGBoost. After preprocessing and PCA, class imbalance was addressed with AllKNN and Tomek Links. Both models achieved high accuracy, and key spectral bands-Amide I, II, and III-were consistently highlighted, supporting their relevance in oral cancer detection.
- 21 September 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-3
Discriminating Oral Squamous Cell Carcinoma via $\mu$ FTIR Spectroscopy Imaging and Tree-Based ModelsThis study evaluates the classification of oral squamous cell carcinoma (OSCC) using micro-FTIR hyperspectral imaging and two tree-based models: Random Forest and XGBoost. After preprocessing and PCA, class imbalance was addressed with AllKNN and Tomek Links. Both models achieved high accuracy, and key spectral bands-Amide I, II, and III-were consistently highlighted, supporting their relevance in oral cancer detection.
- 11 November 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-3
A Deep Neural Network Approach for Oral Squamous Cell Carcinoma Identification
Early detection and diagnosis of oral squamous cell carcinoma (OSCC) are essential for improving patient outcomes. This study presents the development of a deep neural network model trained to classify OSCC using hyperspectral FTIR spectroscopy imaging. The network's performance was evaluated using accuracy, precision, recall, and F1-score metrics. The model demonstrated high effectiveness in classifying unfolded OSCC hyperspectral images, showing potential as a valuable tool for supporting diagnostic processes in clinical settings.
- 11 November 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-3
A Deep Neural Network Approach for Oral Squamous Cell Carcinoma Identification
Early detection and diagnosis of oral squamous cell carcinoma (OSCC) are essential for improving patient outcomes. This study presents the development of a deep neural network model trained to classify OSCC using hyperspectral FTIR spectroscopy imaging. The network's performance was evaluated using accuracy, precision, recall, and F1-score metrics. The model demonstrated high effectiveness in classifying unfolded OSCC hyperspectral images, showing potential as a valuable tool for supporting diagnostic processes in clinical settings.
- 11 November 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-3
The Role of the Student Chapter in Scientific Outreach: An Experience Report in the Field of Optics and Photonics in Brazil
This paper reviews the activities conducted by a student chapter of optics and photonics in Brazil during the 2023–2024 academic year. The initiative played a significant role within its host institution, contributing to scientific outreach and academic enrichment. The paper assesses key initiatives and their impact on both the broader community and the institution itself. We highlight several events organized during this period, which engaged over 1,500 participants. Additionally, the paper examines the benefits derived from these activities for the institutions, the participants, and the general public, emphasizing their contribution to the dissemination of optics and photonics within the community.
- 11 November 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-3
The Role of the Student Chapter in Scientific Outreach: An Experience Report in the Field of Optics and Photonics in Brazil
This paper reviews the activities conducted by a student chapter of optics and photonics in Brazil during the 2023–2024 academic year. The initiative played a significant role within its host institution, contributing to scientific outreach and academic enrichment. The paper assesses key initiatives and their impact on both the broader community and the institution itself. We highlight several events organized during this period, which engaged over 1,500 participants. Additionally, the paper examines the benefits derived from these activities for the institutions, the participants, and the general public, emphasizing their contribution to the dissemination of optics and photonics within the community.