Roundworms and whipworms are examples of intestinal parasites that feeds on its host. These parasites pose a threat and danger to the health of both non-domesticated and domesticated dogs, this concern also extends to dog owners especially to young and elderly. In this study, a deep learning model named Convolutional Neural Network is used in detection and identification of roundworm and whipworm eggs. The CNN trained model is build using AlexNet architecture. It is trained using the image dataset collected from Vet Central Lab Philippines and tested using the image dataset captured using the prototype which is the raspberry pi camera module mounted to the microscope. To determine the efficiency and correctness of the system, confusion matrix is applied. The system obtained an accuracy of 96% and 90.74% in classifying whipworm and roundworm eggs respectively. The study shows promising result that AlexNet CNN architecture can be used in detection of intestinal parasite eggs.