A Deep Neural Network Approach for Oral Squamous Cell Carcinoma Identification
Daniella Lúmara Peres, Gleice C. M. Germano, Daniela de Fátima Teixeira da Silva, Luciano Bachmann, Leandro Luongo Matos, Joaquim Cezar Felipe, Thiago Martini Pereira, Denise Maria Zezéll · 2024
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.