# A Framework based on Deep Neural Networks to Extract Anatomy of Mosquitoes from Images

arXiv: https://arxiv.org/abs/2007.11052 · HTML: https://mlsanity.com/paper/2007.11052/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [On the use of uncertainty in classifying Aedes Albopictus mosquitoes](https://mlsanity.com/paper/2110.15912/) (2021)
- [mAedesID: Android Application for Aedes Mosquito Species Identification using Convolutional Neural Network](https://mlsanity.com/paper/2305.07664/) (2023)

## Abstract (arXiv)

We design a framework based on Mask Region-based Convolutional Neural Network (Mask R-CNN) to automatically detect and separately extract anatomical components of mosquitoes - thorax, wings, abdomen and legs from images. Our training dataset consisted of 1500 smartphone images of nine mosquito species trapped in Florida. In the proposed technique, the first step is to detect anatomical components within a mosquito image. Then, we localize and classify the extracted anatomical components, while simultaneously adding a branch in the neural network architecture to segment pixels containing only the anatomical components. Evaluation results are favorable. To evaluate generality, we test our architecture trained only with mosquito images on bumblebee images. We again reveal favorable results, particularly in extracting wings. Our techniques in this paper have practical applications in public health, taxonomy and citizen-science efforts.