Advancing Thyroid Pathologies Detection with Recurrent Neural Networks and Micro-FTIR Hyperspectral Imaging

• Main Authors: De Freitas Oliveira Baffa, Matheus, Bachmann, Luciano, Zezell, Denise Maria, Pereira, Thiago Martini, Deserno, Thomas Martin, Felipe, Joaquim Cezar
• Format: Conference Proceeding
• Language: English
• Subjects: Biology; classification; computational pathology; Computer vision; deep learning; hyperspectral imaging; Medical diagnosis; Medical diagnostic imaging; Pathology; Recurrent neural networks; Sensitivity; thyroid cancer
• ISSN: 2372-9198
• DOI: 10.1109/CBMS58004.2023.00288

Thyroid disorders are a complex group of diseases that require an accurate diagnosis for effective treatment. Fine-needle aspiration biopsies can assist in detecting many thyroid diseases. These materials can be analyzed visually using traditional computer vision methods, despite the limitations of complex samples. To address this problem, we propose a novel approach that uses hyperspectral imaging (HSI) to analyze thyroid biological samples. HSI measures the absorbance of infrared light by biological samples using a micro Fourier transform infrared spectroscopy (micro-FTIR) and converts this data into hyperspectral images. In this study, we used HSI to train and validate a recurrent neural network to classify thyroid samples as healthy, cancerous, or goiter. Our experiments, based on the k-fold cross-validation, achieved an overall accuracy of 96.88%, a sensitivity of 96.87%, and a specificity of 98.45%. These results demonstrate the potential of hyperspectral imaging as a tool to assist pathologists in the diagnosis of thyroid disease.