A computed tomography scan is a process that uses computers and X-ray machines, which rotate to create cross-sectional images of the body. The images provided by the CT scans are more accurate and detailed in comparison to standard X-ray images. The detail in the images can be increased by administering a chemical dye called contrast. A medical practitioner can administer the dye with prior permission from the patient as there is a risk of having side effects such as cancerous disease and headaches. Our study aims at providing a classification model using a CNN learning algorithm to classify images based on the age of a person and if contrast material is used. A practitioner hence can get prior knowledge about trends in CT scan images and if administering contrast material would affect the image in any way. Our proposed model provides an accuracy of 89.99% on any test CT image.