Malaria Parasite Identification from Red Blood Cell Images Using Transfer Learning Models

Abstract

Malaria is a dangerous viral disease caused by Plasmodium protozoan parasites that are spread by the bite of an infected female Anopheles mosquito. This pandemic disease's fast and precise identification is essential for effective treatment. The most reliable method for diagnosing malaria is a microscopic examination of a thick and thin blood smear, which looks for the parasite and counts the number of infected cells. The ability to wholly or partially automate the identification of the disease using the information in medical images highlights the critical role that computer-aided diagnosis plays in modern medicine, in which machine learning and deep learning play a critical role. In this study, we have presented an in-depth overview of the techniques and methods used to diagnose the malaria parasite through blood slides automatically. One of the techniques is using transfer learning models to detect the malaria parasite. We have compared the performance of transfer learning models on identifying infected malaria cells by feeding the models a large dataset of uninfected and parasite cell images. The results show that the DensNet models have the edge over the other models, with DenseNet-201 achieving the highest accuracy and F1 score of 0.9339 and 0.9321, respectively. Also, DenseNet-169 outperformed the other models with 0.9594 in precision, and finally, Densenet-121 had the highest recall with 0.9490.

The impact of image augmentation techniques of MRI patients in deep transfer learning networks for brain tumor detection

“…The With a balance between complexity and efficiency, InceptionV3 excels in image classification tasks, boasting superior performance on benchmark datasets and establishing itself as a pivotal advancement in the deep learning landscape. Figure 4 illustrates the InceptionV3's architecture [31].…”