Ovarian cancer is challenging due to poor detection rates and high mortality. Multispectral fluorescence imaging (MFI) has recently become a favorable method for cancer characterization. By utilizing MFI along with a characterized ovarian cancer mouse model and human fallopian tube histology sections, we were able to study cancer in its earliest stages with a promising modality for early disease detection. Fluorescence images with various emission combinations from 280nm to 550nm excitation and reflectance images from 320nm to 550nm were taken of 8-week-old mouse ovarian tissues. Human fallopian tube histology slides were also imaged with the same fluorescence images. Disease characterization was studied using Quadratic Discriminant Analysis (QDA) on image grayscale intensities for both tissues as well as the grey-level co-occurrence matrix (GLCM) in human tissue slides in order to determine a classification group with the highest predictive merit. Both tissues were able to be classified with greater than 80% accuracy, suggesting promise for MFI as a potential diagnostic candidate.