Real-life evaluation of deep learning models trained on two datasets for Plasmodium falciparum detection with thin blood smear images at 500x magnification

Abstract

Malaria is a fatal disease transmitted by bites from mosquito-type vectors. Biologists examined blood smears under a microscope at high magnification (1000 × ) to identify the presence of parasites in red blood cells (RBCs). Such an examination is laborious and time-consuming. Moreover, microscopists sometimes have difficulty identifying parasitized RBCs due to a lack of skill or practice. Deep learning, especially convolutional neural networks (CNNs) applied for malaria diagnosis, are able to identify complex features of a large number of medical images.The proposed work focuses on the construction of a dataset of blood components images representative of the diagnostic reality captured from 202 patients at 500x magnification. We evaluated through a cross-validation study different deep learning networks for the classification of Plasmodium falciparum-infected RBCs and uninfected blood components. These models include a custom-built CNN, VGG-19, ResNet-50 and EfficientNet-B7. In addition, we conducted the same experiments on a public dataset and compared the performance of the resultant models through a patient-level inference including 200 extra patients. The models trained on our dataset show better performance in terms of generalization and achieved better accuracy, sensitivity and specificity scores of 99.7%, 77.9% and 99.8%, respectively.

Keywords

• Malaria diagnosis
• Thin blood smear
• Microscopic image
• Deep transfer learning
• Convolutional neural network