Performance of deep convolutional neural network approaches and human level in detecting mosquito species

Abstract

AbstractRecently, mosquito-borne diseases have been a significant problem for public health worldwide. These diseases include dengue, ZIKA and malaria. Reducing disease spread stimulates researchers to develop automatic methods beyond traditional surveillance Well-known Deep Convolutional Neural Network, YOLO v3 algorithm, was applied to classify mosquito vector species and showed a high average accuracy of 97.7 per cent. While one-stage learning methods have provided impressive output in Aedes albopictus, Anopheles sinensis and Culex pipiens, the use of image annotation functions may help boost model capability in the identification of other low-sensitivity (< 60 per cent) mosquito images for Cu. tritaeniorhynchus and low-precision Ae. vexans (< 80 per cent). The optimal condition of the data increase (rotation, contrast and blurredness and Gaussian noise) was investigated within the limited amount of biological samples to increase the selected model efficiency. As a result, it produced a higher potential of 96.6 percent for sensitivity, 99.6 percent for specificity, 99.1 percent for accuracy, and 98.1 percent for precision. The ROC Curve Area (AUC) endorsed the ability of the model to differentiate between groups at a value of 0.985. Inter-and intra-rater heterogeneity between ground realities (entomological labeling) with the highest model was studied and compared to research by other independent entomologists. A substantial degree of near-perfect compatibility between the ground truth label and the proposed model (k = 0.950 ± 0.035) was examined in both examinations. In comparison, a high degree of consensus was assessed for entomologists with greater experience than 5-10 years (k = 0.875 ± 0.053 and 0.900 ± 0.048). The proposed YOLO v3 network algorithm has the largest capacity for support-devices used by entomological technicians during local area detection. In the future, introducing the appropriate network model based methods to find qualitative and quantitative information will help to make local workers work quicker. It may also assist in the preparation of strategies to help deter the transmission of arthropod-transmitted diseases.