An Explainable Artificial Intelligence Integrated System for Automatic Detection of Dengue From Images of Blood Smears Using Transfer Learning

Dengue fever is a rapidly increasing mosquito-borne ailment spread by the virus DENV in the tropics and subtropics worldwide. It is a significant public health problem and accounts for many deaths globally. Implementing more effective methods that can more accurately detect dengue cases is challenging. The theme of this digital pathology-associated research is automatic dengue detection from peripheral blood smears (PBS) employing deep learning (DL) techniques. In recent years, DL has been significantly employed for automated computer-assisted diagnosis of various diseases from medical images. This paper explores pre-trained convolution neural networks (CNNs) for automatic dengue fever detection. Transfer learning (TL) is executed on three state-of-the-art CNNs - ResNet50, MobileNetV3Small, and MobileNetV3Large, to customize the models for differentiating the dengue-infected blood smears from the healthy ones. The dataset used to design and test the models contains 100x magnified dengue-infected and healthy control digital microscopic PBS images. The models are validated with a 5-fold cross-validation framework and tested on unseen data. An explainable artificial intelligence (XAI) approach, Gradient-weighted Class Activation Mapping (GradCAM), is eventually applied to the models to allow visualization of the precise regions on the smears most instrumental in making the predictions. While all three transferred pre-trained CNN models performed well (above 98% overall classification accuracy), MobileNetV3Small is the recommended model for this classification problem due to its significantly less computationally demanding characteristics. Transferred pre-trained CNN based on MobileNetV3Small yielded Accuracy, Recall, Specificity, Precision, F1 Score, and Area Under the ROC Curve (AUC) of 0.982 +/- 0.011, 0.973 +/- 0.027, 0.99 +/- 0.013, 0.989 +/- 0.015, 0.981 +/- 0.012 and 0.982 +/- 0.012 respectively, averaged over the five folds on the unseen dataset. Promising results show that the developed models have the potential to provide high-quality support to haematologists by expertly performing tedious, repetitive, and time-consuming tasks in hospitals and remote/low-resource settings.