Redes complexas para classificação de alto nível de assinaturas vibracionais moleculares

Abstract

Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy is a promising technique for rapid and non-invasive diagnostics, yet its clinical application is often constrained by significant analytical challenges. The high dimensionality, complexity, and spectral overlap inherent in real-world data make it difficult to distinguish between different pathological conditions, a task where both conventional classifiers and deep learning models have shown limitations. To address these challenges, we propose ATLA (ATR-FTIR Topological Learning Analysis), a novel framework that integrates topological, structural, and physical information for enhanced spectral classification. Our methodology leverages the feature extraction capabilities of Complex Networks in synergy with traditional classifiers (Support Vector Machines and Linear Discriminant Analysis) and a state-of-the-art deep learning architecture (Convolutional Neural Network-BiLSTM). We assessed ATLA's performance on challenging real-world ATR-FTIR datasets for the diagnosis of COVID-19, Hepatitis B and D, and Chagas Disease. Our findings indicate that ATLA significantly improves predictive outcomes across key metrics, including accuracy, precision, sensitivity, specificity, and F1-score. Beyond predictive power, ATLA also enhances model interpretability by using SHAP (SHapley Additive exPlanations) to highlight the most relevant spectral bands and topological features driving the classification. Ultimately, ATLA emerges as a robust and effective approach, poised to elevate spectral diagnostics to a new level of accuracy and interpretability.