DOI: 10.5061/dryad.b8gtht7mx
title: Data from: Robust mosquito species identification from diverse body and wing images using deep learning

description: Mosquito-borne diseases are a major global health threat. Traditional
 morphological or molecular methods for identifying mosquito species often
 require specialized expertise or expensive laboratory equipment. The use
 of Convolutional Neural Networks (CNNs) to identify mosquito species based
 on images may offer a promising alternative, but their practical
 implementation often remains limited. This study explores the
 applicability of CNNs in classifying mosquito species. It compares the
 efficacy of body and wing depictions across three image collection
 methods: a smartphone, macro-lens attached to a smartphone and a
 professional stereomicroscope. The study included 796 specimens of four
 morphologically similar Aedes species, Aedes aegypti,
 Ae. albopictus, Ae. koreicus, and Ae. japonicus japonicus. The
 findings of this study indicate that CNN models demonstrate superior
 performance in wing-based classification 87.6% (CI95%: 84.2 - 91.0)
 compared to body-based classification 78.9% (CI95%: 77.7 - 80.0).
 Nevertheless, there are notable limitations of CNNs as they perform
 reliably across multiple devices only when trained specifically on those
 devices, resulting in an average decline of mean accuracy by 14%, even
 with extensive image augmentation. Additionally, we also estimate the
 required training data volume for effective classification, noting a
 reduced requirement for wing-based classification in comparison to
 body-based methods. Our study underscores the viability of both body and
 wing classification methods for mosquito species identification while
 emphasizing the need to address practical constraints in developing
 accessible classification systems.
description: We collected images from 797 female mosquito specimens with 198 -
 200 specimens of four different species: <em>Aedes
 aegypti</em>, <em>Ae. albopictus</em>, <em>Ae.
 koreicus</em> and <em>Ae. japonicus japonicus</em>
 (<em>Ae. japonicus</em>) (Table 1). All specimens were reared
 under standardized conditions in the arthropod rearing facility at the
 Bernhard Nocht Institute for Tropical Medicine, Hamburg. Each specimen was
 photographed using three different devices: a smartphone (iPhone SE 3rd
 Generation, Apple Inc., Cupertino, USA), a macro-lens (Apexel-25MXH,
 Apexel, Shenzhen, China) connected to the same smartphone, and a
 stereomicroscope (Olympus SZ61, Olympus, Tokyo, Japan) with an attached
 camera (Olympus DP23, Olympus, Tokyo, Japan). In the following text, we
 will refer to the smartphone as a “phone”, the smartphone with a macro
 lens attachment as “macro-lens” or “macro”, and the stereomicroscope as
 “microscope” or “micro”. For the “body” dataset, the
 complete mosquitoes were photographed with all three devices in the same
 orientation to guarantee the visibility of identical features in all the
 pictures (example images can be found in the appendix: Image comparison).
 Subsequently, for the “wing” dataset, the left and right wings were
 mounted on a microscope slide using the embedding medium Euparal (Carl
 Roth, Karlsruhe, Germany) and photographed with the macro-lens and
 microscope. Due to the small size of the wings, image capture through the
 phone only was not feasible. The left wing of each specimen was used. If
 the left wing was damaged the right wing was used as an
 alternative. Image capture for <em>Ae.
 aegypti</em>, <em>Ae. albopictus</em> and <em>Ae.
 koreicus</em> was done in batches of 50 to reduce biases during the
 image capture process, e.g. light conditions in the room. Images of
 <em>Ae. japonicus</em> were collected after the initial data
 collection process was completed, because we aimed to add another
 morphologically similar species to the study to increase its robustness.
 All images were manually cropped to remove as much background as possible
 and subsequently downscaled to a size of 300x300 pixels. To create images
 with a ratio of 1:1, images were cropped with padding. The complete image
 dataset was randomly partitioned into training (70%), validation (15%),
 and testing (15%) subsets (Table 1). Thereby, the dataset split was
 determined based on mosquito specimen rather than individual images to
 ensure a stringent division between the datasets.
description: # Data from: Robust mosquito species identification from diverse body and
 wing images using deep learning
 [https://doi.org/10.5061/dryad.b8gtht7mx](https://doi.org/10.5061/dryad.b8gtht7mx) Mosquito-borne diseases are a major global health threat. Traditional morphological or molecular methods for identifying mosquito species often require specialized expertise or expensive laboratory equipment. The use of Convolutional Neural Networks (CNNs) to identify mosquito species based on images may offer a promising alternative, but their practical implementation often remains limited. This study explores the applicability of CNNs in classifying mosquito species. It compares the efficacy of body and wing depictions across three image collection methods: a smartphone, macro-lens attached to a smartphone and a professional stereomicroscope. The study included 796 specimens of four morphologically similar Aedes species, Aedes aegypti, Ae. albopictus, Ae. koreicus, and Ae. japonicus japonicus. The findings of this study indicate that CNN models demonstrate superior performance in wing-based classification 87.6% (CI95%: 84.2 - 91.0) compared to body-based classification 78.9% (CI95%: 77.7 - 80.0). Nevertheless, there are notable limitations of CNNs as they perform reliably across multiple devices only when trained specifically on those devices, resulting in an average decline of mean accuracy by 14%, even with extensive image augmentation. Additionally, we also estimate the required training data volume for effective classification, noting a reduced requirement for wing-based classification in comparison to body-based methods. Our study underscores the viability of both body and wing classification methods for mosquito species identification while emphasizing the need to address practical constraints in developing accessible classification systems. ## Description of the data and file structure Images are provided in a 896x896 pixel resolution. Two folders are given. **DepictionDataset**: Files used for the Depiction and Data Demand experiments. Only images captured via macro lens and microscope. **DeviceDataset**: Files used for the Device Comparison experiments. All images split by device Both folders are split into training, validation and testing. Hereafter folders then are split by species (AE: *Aedes aegypti*, AL: *Ae. albopictus*, KO: *Aedes koreicus*, JA: *Ae. japonicus*.). Files are named according to this scheme: _.png. If images is taken by smartphone an image number is also attached. ## Code Code can be found on the projects GitHub page: [https://github.com/KNolte19/Mosquito-CNN-Comparison](https://github.com/KNolte19/Mosquito-CNN-Comparison)