Malaria Cell Detection Using Machine Learning

Abstract

Abstract: Malaria is a major public health issue that affects people all around the world. The diagnosis of red blood cells contaminated with insects under a microscope by a skilled specialist is a typical way of diagnosing malaria. This method does not function effectively, and the diagnosis is made based on the test taker's experience and expertise. Malaria blood tests have been using automated imaging technologies based on machine learning for early detection. However, thus far, practical performance is insufficient. The convolutional neural network CNN is used in this paper to present an innovative and resilient machine learning model. for automatically distinguishing single cells in tiny blood smears from normal microscope slides, such as infected or non-infected with this virus. Our new CNN model's average accuracy for 16 layers is 97.37 percent based on ten-fold confirmation using 27,578 single-image pictures. In the same photos, the learning transfer model only gets 91.99 percent. All performance indicators, such as sensitivity (96.99 percent vs 89.00 percent), clarity (97.75 percent vs 94.98 percent), accuracy (97.73 percent vs 95.12 percent), F1 school (97.36 percent vs 90.24) percent, and Matthew's correlation coefficient, demonstrate that the CNN model outperforms the transfer learning model (94.75 percent vs 85.25 percent). Index terms: Deep Learning; Convolutional Neural Networks; Malaria; Computer-Aided Diagnostics; Machine Learning