Abstract:
Despite global health advancements, mosquito borne diseases are still life threating
and prevalent, with dengue and malaria contributing to thousands of deaths each year.
However, the responsibility for transmitting these specific diseases does not lie with
all mosquito species globally. Detecting mosquito species from their wingbeats acoustic
data can be very effective, however, it is a challenging task to accurately distinguish
specific mosquito species based on their wingbeat patterns. Our acoustic-based
deep learning model for mosquito species classification uses the combination of sinc
layer, one dimensional convolutional layer and long short-term memory (LSTM) layer
to learn features from raw audio signals and capture the temporal dependencies between
them strikingly outperforming existing methods. We achieve test accuracy of
93.59% precision of 0.9363, Recall of 0.9311 and F1 Score of 0.9335. Our model has
significant potential for low-cost, non-invasive, and high-throughput identification of
mosquito species, which can play a role on the prevention and control of mosquitoborne
diseases. By utilizing the unique wingbeat patterns of mosquitoes, our model
enables early identification and monitoring of disease-carrying species. This early detection
allows for timely implementation of targeted interventions, focusing resources
on the specific mosquito species responsible for disease transmission in a particular region.
Additionally, acoustic-based models enhance surveillance systems by providing
real-time tracking and monitoring of mosquito populations. This data-driven approach
facilitates rapid response to potential disease outbreaks, enabling public health authorities
to implement effective control measures and reduce the burden of mosquito-borne
diseases on communities.
