Classification of Malaria-Infected Cells using Convolutional Neural Networks

The objective of this project is to develop a robust Convolutional Neural Network (CNN) model for the automated classification of malaria-infected cells in microscopic images. By leveraging deep learning techniques, the aim is to accurately differentiate between infected and uninfected cells, enabling rapid and cost-effective diagnosis. This project intends to contribute to the early detection and monitoring of malaria, particularly in regions with limited access to skilled medical professionals. The CNN model will be trained on a dataset of labeled cell images, and its performance will be assessed in terms of accuracy, sensitivity, and specificity to provide a valuable tool for aiding healthcare practitioners in malaria diagnosis and research efforts.