Tuberculosis severity classification from exhaled breath using an electronic nose system with Inception-1D and ResNet-1D - Dava Aulia; Riyanarto Sarno; Muhammad Rivai; Muhammad Amin; Alfian Nur Rosyid; Kelly Rossa Sungkono (Institute of Advanced Engineering and Science, 2026)
Exhaled breath contains volatile organic compounds (VOCs) that can be analyzed for tuberculosis (TB) detection. Electronic nose systems have demonstrated promise for this application; however, accurately distinguishing between healthy individuals and TB patients with different severity levels, namely, low and high TB, remains challenging and requires advanced deep-learning methods. Unlike previous studies that focused on binary TB detection, this study proposes an electronic nose system combined with one-dimensional deep learning models, including residual network (ResNet), visual geometry group (VGG), EfficientNet, and Inception architectures, for multiclass classification of healthy individuals and TB severity levels using exhaled-breath analysis. The results show that Inception-1D and ResNet-1D achieve the best performance for healthy and TB classification, each attaining an F1-score of 94.99%. For TB severity classification, ResNet-1D outperforms other models with an F1-score of 68.50%. Meanwhile, Inception-1D yields the highest performance on the healthy and TB severity classification dataset, with an F1-score of 74.07%. Moreover, dimensionality reduction using principal component analysis (PCA) reduces the healthy and TB dataset to ten principal components and improves the F1-score to 95.64% with Inception-1D. Overall, the proposed framework successfully captures distinctive gas sensor-response patterns associated with TB presence and severity and may support clinical decision-making in resource-limited healthcare settings.