Abstract
Automated early breast cancer detection has been credited as a lifesaver. In this work, an innovative approach based on the persistent homology diagram (PHD) is proposed. Every mammogram is processed using topological data analytic methods to generate its PHD and then the resized PHDs are analyzed for similarity using Earth mover’s distance (EMD). The mammogram corpora obtained from SRM-Chennai Hospital with requisite clearance are analyzed for preliminary results. EMD from our earlier investigations has shown promising results when implemented independently on mammograms. We believe that knowledge of the topological structures obtained using the persistent diagrams can help identify the important structures and signatures in a mammogram and focus on a relevant region of interest. This additional processing layer can provide some interesting insights to offer while implementing an automated disease-tagging web service for breast cancer. The PHD will form the rationale for devising the strategy aimed to resolve the issue of missed- and misdiagnosis of breast cancer resulting in poor clinical prognosis at the community level. Furthermore, a web service-based or mobile-health approach promises to provide fruganomic point of care disease-tagging to the stakeholders at the bottom-end of the healthcare ecosystems residing in remote locations across the Indian subcontinent. The development of multimodal multisensory computational platforms incorporating digital signals from PHD-based image analytics of mammograms and novel biomarkers will form the rationale for large-scale screening of breast cancer patients at the community level.
