Classification of Malaria-Infected Cells using Convolutional Neural Networks

Project Code :TCMAPY1015

Objective

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.

Abstract

Classification of Malaria-Infected Cells using Convolutional Neural Networks employs advanced machine learning techniques to enhance malaria diagnosis. Leveraging Convolutional Neural Networks (CNNs), the study focuses on automating the identification of malaria-infected cells in blood smears. The CNN model is trained on a comprehensive dataset, enabling accurate classification of infected and uninfected cells. This automated approach not only accelerates the diagnosis process but also reduces human errors associated with manual examination. The project contributes to the field of medical image analysis, showcasing the potential of CNNs in improving the efficiency and accuracy of malaria detection.

Keywords: Malaria dataset, Convolutional Neural Networks

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.