Malaria Cell Image Classification using Deep Learning
P Dinesh Saravan, S. M., S Venkata Mahesh, Tripty Singh
Abstract
In this study we are going to have the deep learning system doing the classifying work for blood slides of cells either infected with malaria or not infected. The foundation of our method involves reflection on the data before processing to enhance quality of images and also in identification of the target cells through extraction of specific features. A model that is fine-tuned to classify blood smears through images with malaria or without is the basis for the early detection. The possibility of this revolutionizes the diagnostics is incredible, especially in conditions of scarcity. With the use of these technologies, we can be able to turn the diagnosis from long and error-prone diagnostic procedures to faster and precise ones. Such improvement will boost the whole malaria control and the managing health sector. We have developed an approach the goal of which is to be open to innovations with a possibility to update variables to the different settings of health care. With the aim to increase its scalability and useability, we develop an AI- powered deep learning-based diagnosis to popularize its among people. Furthermore, we are still working to make our models much stronger and immune to any forms of errors, thus becoming significant assets in the anti-malaria campaign.