Cancer is the second leading cause of death in the United States. To fight cancer, we need early detection and new treatments to lower death rates and improve survival. Recently, Chromatin-Sensitive Partial Wave Spectroscopic (csPWS) microscopy has become a key tool for early cancer detection and treatment monitoring. This technique measures changes in chromatin structure within cell nuclei at the nanoscale level. To make it easier to analyze chromatin packing and nuclear shapes, software that automates cell selection and nuclei segmentation is needed. Current manual methods are slow, complicated, and vary from user to user. Manual segmentation is also challenging due to the unique features of label-free csPWS images. This project aims to create an Artificial Intelligence(AI)-based segmentation technique that quickly and accurately selects nuclei from various cancer cell lines and imaging conditions. This will help streamline chromatin analysis for early detection and treatment of cancers using csPWS data. The project will also develop an AI algorithm that predicts early cancer and tracks treatment responses using spectral information from raw csPWS images. These AI tools will expand the use of csPWS microscopy, making it accessible for cancer screening, diagnosis, and treatment. This is a significant step toward better health outcomes. The project will provide undergraduate and graduate students in electrical, computer, and biomedical engineering with valuable research train