Colorimetric assays are an important tool in point-of-care testing that offer several advantages to traditional testing methods such as rapid response times and inexpensive cost. A factor that currently limits the portability and accessibility of these assays is a method to objectively determine the results of these assays. Current solutions consist of creating a test reader that standardizes the conditions the strip is under before measuring in some way. However, this increases the cost and decreases the portability of these assays. The focus of this study is to train a convolutional neural network (CNN) that can objectively determine results of colorimetric assays under varying conditions. To ensure the flexibility of the model to several types of colorimetric assays, three models are trained on the same CNN. The images these models are trained on consist of positive and negative images of ETG, fentanyl, and HPV antibody strips taken under different lighting and background conditions. A fourth model is trained on an image set composed of all three strip types. The results show the model can predict positive and negative results to a high level of accuracy.