Image Dataset of Paper-Based Biosensor for CA19-9 Detection Using Melanin Nanoparticles: Machine Learning and Deep Learning Analysis for Pancreatic Cancer Biomarker Monitoring

This dataset focuses on the development and evaluation of a paper-based colorimetric biosensor utilizing natural melanin nanoparticles derived from cuttlefish for the detection of the pancreatic cancer biomarker CA 19-9. The melanin nanoparticles were extracted, purified, and functionalized with glutaraldehyde to enable antibody immobilization, as confirmed by ATR-FTIR and XPS analyses. Anti-CA 19-9 antibodies were conjugated to the functionalized surface, and the biosensor's interaction with varying concentrations of CA 19-9 solutions—specifically 0.025%, 0.05%, 0.075%, 0.1%, 1%, 2%, 3%, 4%, and 5%—produced distinct color changes. These changes were analyzed using optical readers and digital image processing techniques. The dataset includes images captured via smartphone, featuring both control (represents the blank, unmodified surface) and target regions (the modified surface that interacts with the CA 19-9 solution) to minimize environmental variability, and provides quantitative measurements of color intensity changes corresponding to the CA 19-9 concentrations. The biosensor demonstrated high selectivity, reliability, and sensitivity, validated through repeated measurements and cross-reactivity assays. This dataset supports the development of machine learning and image processing algorithms for accurate, portable, and cost-effective biomarker detection, with potential applications in early cancer diagnosis and monitoring.