"Deep learning approach for detection of Dengue fever from the microsco" by Hilda Mayrose, Niranjana Sampathila et al.
 

Deep learning approach for detection of Dengue fever from the microscopic images of blood smear

Document Type

Conference Proceeding

Publication Title

Journal of Physics: Conference Series

Abstract

Dengue virus (DENV), known to cause dengue fever is a global public health concern. A safe and effective anti-viral drug or vaccine that can protect humans from dengue fever currently does not exist. Today, severe dengue has become a leading cause of serious illness in most Asian and Latin American countries. This digital pathology-related research focuses on the automatic detection of dengue by utilizing digital microscopic peripheral blood smears (PBS). This paper explored pre-trained convolution neural network (CNN) architectures for automatic dengue fever detection. Transfer learning (TL) was performed on two widely used pre-trained CNNs - SqueezeNet and GoogleNet, and employed to differentiate the dengue-infected and normal blood smears. The last few layers were replaced and retrained to customize the architectures for this task. Leishman's stained dengue-infected and normal control 100x magnified PBS images were included in the study. The best performance was rendered by GoogleNet (Learn Rate, 0.0001; Batch Size, 8) with an Accuracy 91.30%, Sensitivity 84.62%, Specificity 100%, Precision 100%, and F1 score 91.67%. Promising results show that this approach can be an essential adjunct to other clinical methods, namely CBC test & NS1 antigen capture, and can significantly support dengue diagnosis in low-resource setups.

DOI

10.1088/1742-6596/2571/1/012005

Publication Date

1-1-2023

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