Joaquim Cezar Felipe

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.

This 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.

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.

Brain tumor corresponds to a group of diseases in which abnormal cells grow exponentially within the brain, the most important organ of the central nervous system. This disease is responsible for thousands of deaths worldwide. Early diagnosis and treatment increase the chance of survival. The most common technique to detect such tumors is analyzing MRI scans which carry subjectivity and lacks reproducibility. In order to improve the accuracy of such technique, a computer-aided system has been proposed, using Machine Learning and Image Processing to classify scans, characterizing such tumors, and acting as a second opinion. This paper benchmark several machine learning methods, such as ensemble and neural approaches, along with Radiomic features to classify MRI acquired on T1, T2, and FLAIR modalities, between healthy, glioma, meningioma, and pituitary tumor. Our experiments were conducted following K-Fold Cross-Validation and the best results were achieved by XGBoost and Deep Neural Network, in which XGBoost achieved an average accuracy of 88.51%and a sensitivity of 89.10 %, and the Deep Neural Network achieved 87.09% and a sensitivity of 87.38%. Such Computer Vision technique may assist radiologists to provide an accurate and efficient tumor diagnosis using MRI.