This research focuses on the benefits of computer vision enhancement through use of an image pre-processing optimization algorithm in which numerous variations of prevalent image modification tools are applied independently and in combination to specific sets of images. The class with the highest returned precision score is then assigned to the feature, often improving upon both the number of features captured and the precision values. Various transformations such as embossing, sharpening, contrast adjustment, etc. can bring to the forefront and reveal feature edge lines previously not capturable by neural networks, allowing potential increases in overall system accuracy beyond typical manual image pre-processing. Similar to how neural networks determine accuracy among numerous feature characteristics, the enhanced neural network will determine the highest classification confidence among unaltered original images and their permutations run through numerous pre-processing and enhancement techniques.