Mosquito Larvae Image Classification Based on DenseNet and Guided Grad-CAM

The surveillance of Aedes aegypti and Aedes albopictus mosquito to avoid the spreading of arboviruses that cause Dengue, Zika and Chikungunya becomes more important, because these diseases have greatest repercussions in public health in the significant extension of the world. Mosquito larvae identification methods require special equipment, skillful entomologists and tedious work with considerable consuming time. In comparison with the short mosquito lifecycle, which is less than 2 weeks, the time required for all surveillance process is too long. In this paper, we proposed a novel technological approach based on Deep Neural Networks (DNNs) and visualization techniques to classify mosquito larvae images using the comb-like figure appeared in the eighth segment of the larva’s abdomen. We present the DNN and the visualization technique employed in this work, and the results achieved after training the DNN to classify an input image into two classes: Aedes and Non-Aedes mosquito. Based on the proposed scheme, we obtain the accuracy, sensitivity and specificity, and compare this performance with existing technological approaches to demonstrate that the automatic identification process offered by the proposed scheme provides a better identification performance.