The proponents for this study developed a descriptive developmental type of research, by constructing a software application with the use of YOLOv4-tiny algorithm to be able to detect certain abnormalities within the colon, namely Polyps and Diverticula, which are the leading causes of cancer in the colon and rectal area. The respondents of this study are chosen through purposive sampling, to which includes each of their own expertise in helping for software development. The respondents are Medical Practitioners, for their expertise in the subject relating to the medical field and was served as the main utilizers of the system, CS/IT Professionals for the purpose of critiquing the software application and its intended modules, and lastly CS/IT Students provided their recommendations and suggestions for improving the software application. Based on the survey derived from the categories of ISO 25010, the total average accumulated from each respondent group is valued at 4.08 for medical practitioners, 4.62 from CS/IT Professionals, and 4.32 from CS/IT Students. The software application was developed to be light-weight, enabling a capability of real-time detection. The average time for the system to process uploaded colonoscopy videos were dependent on the size and length of the provided colonoscopy videos. In the researchers alpha tests, the time it took for the videos to fully process ranges from 3 - 5 minutes. The findings of the study shows that the YOLOv4 algorithm displayed a 90% accuracy in detecting polyps and diverticula with also the implementation of deep CNNs and as well as ReLu activation, thus establishing an intricate relationship between the colon abnormality's features. The researchers successfully created a system that is capable of detecting said Polyps and Diverticula at 90% accuracy by employing the confusion matrix.