Non-small cell lung cancer (NSCLC) results in over 1.8 million deaths worldwide every year; however, most of these deaths are preventable via early diagnosis, which reduces the mortality rate by more than 50%. Currently, physicians use a CT (Computed Tomography) scan as a preliminary method of identifying cancerous tumors. Unfortunately, this visual process of identifying NSCLC scans becomes time-consuming and inaccurate, leading to high misdiagnosis rates. The goal of this project is to create a cloud-based web application that can take an inputted CT scan and identify regions of potentially cancerous tumors at an accuracy >90%. This is accomplished by first standardizing all inputted scans to a standard size and range of pixel values. A 3D CNN is then trained to classify an inputted scan as either "positive" or "negative" for cancer. Class Activation Mapping (CAM) is then used on scans classified as "positive" in order to identify the location(s) of cancerous tumors. This algorithm core is accessed through a cloudbased user interface on AWS allowing physicians to upload and organize their patient's NSCLC scans as well as receive dynamic results based on patient biometric and cancer history background. Using the LungStat platform, an oncologist can simplify the procedure for lung cancer diagnosis by cutting down the average tumor identification time from about 1 hour to a few minutes. Overall, this project establishes a critical tool needed for the accurate diagnosis and treatment of NSCLC, leading to a severe reduction in the death rates caused by lung cancer.