Abstract:
Dengue is one of the highest spreading mosquitoborne diseases in tropical and subtropical regions all over the world. This disease is mainly spread by the mosquito vector called ‘Aedes’. In Sri Lanka, the number of infected patients reported is increasing, and it has become a public health problem. Health Inspectors are using different methods to reduce the spread of this viral disease and one of the main methods used is the fumigation by identifying the Aedes Larvae breeding locations. Currently, this identification is done manually by the specialized health inspectors and it is totally observer-biased and consumes a considerable amount of time, which could lead to false decision making and inefficient identification. The purpose of this research is to build an automated computational model to identify Aedes Larvae in real-time with more accuracy and convenience. Even though there are good results in previous researches done in Convolutional Neural Networks (CNN) on Aedes Larvae identification, the method of capturing Larvae Images is a bit complicated since they have used a Microscope lens of amplification capacity 60-100 times to get the magnified images. In this research, we propose the method of identifying Aedes mosquito larvae with a digital amplification of 8-12 times without using any microscope lenses attached, using ResNet50 CNN. The proposed model will identity the mosquito larvae by their genus ‘Aedes’ or ‘non- Aedes’ using a digital photo taken by a smartphone or camera in the upside of the larvae body. Hence it would help Health Inspectors, even the general public on identifying Aedes Larvae more efficiently, accurately and conveniently than the traditional method. This study shows that the trained model can identify images of Aedes and Non-Aedes Larvae separately with an accuracy of 86.65%. Furthermore, with using pre-processing techniques, the accuracy level can be enhanced to 98.76% for magnified images.
