Early detection of lung diseases using persistent homology

This study investigates the integration of Persistent Homology, a fundamental tool from algebraic topology, with medical image analysis to enhance the interpretability and reliability of lung disease diagnosis from CT scans. Lung diseases remain a major global health concern and early detection is crucial for improving clinical outcomes. However, conventional diagnostic methods and many deep learning-based approaches often suffer from limitations such as subjectivity, lack of interpretability and dependency on large labelled datasets. To address these challenges, this research develops a computational framework that combines image processing techniques with Topological Data Analysis (TDA). CT scan images are pre-processed and transformed into structured data representations, where key points are selected to construct topological spaces suitable for Persistent Homology analysis. Using this approach, stable topological features such as connected components and loops are extracted and represented through persistence diagrams. Based on these topological signatures, a set of rule-based diagnostic criteria is formulated to classify lung conditions into healthy, mild and severe categories. The entire workflow is implemented within a Python-based automated framework, enabling consistent feature extraction and decision-making from raw CT images. The results demonstrate that Persistent Homology provides meaningful and interpretable descriptors of lung structure, allowing objective assessment of disease severity. This study highlights the potential of topology-based methods as a transparent and mathematically grounded alternative to traditional and deep learning approaches in medical imaging. Although the findings are promising, this work represents an initial exploration, and further validation using larger datasets and clinical studies is necessary to fully establish its diagnostic robustness. Nevertheless, the proposed framework offers a promising direction toward data-driven, interpretable, and topology-informed medical diagnostics, with potential applications in early detection and clinical decision support.