This document describes a project to detect malaria parasites in thick blood smear images using a convolutional neural network (CNN) model. It involves two parts: 1) an intensity-based iterative global minimum screening technique to preprocess images and identify parasites, and 2) a customized CNN to classify images as containing parasites or not. The CNN is trained on a dataset of 150 annotated thick blood smear images containing both positive and negative samples. Screenshots demonstrate uploading images, preprocessing them, generating and testing the CNN model, and visualizing results.