Automatic identification of medically important mosquitoes using embedded learning approach-based image-retrieval system

Abstract

Mosquito-borne diseases such as dengue fever and malaria are the top 10 leading causes of death in low-income countries. Control measure for the mosquito population plays an essential role in the fight against the disease. Currently, several intervention strategies; chemical-, biological-, mechanical- and environmental methods remain under development and need further improvement in their effectiveness. Although, a conventional entomological surveillance, required a microscope and taxonomic key for identification by professionals, is a key strategy to evaluate the population growth of these mosquitoes, these techniques are tedious, time-consuming, labor-intensive, and reliant on skillful and well-trained personnel. Here, we proposed an automatic screening, namely the deep metric learning approach and its inference under the image-retrieval process with Euclidean distance-based similarity. We aimed to develop the optimized model to find suitable miners and suggested the robustness of the proposed model by evaluating it with unseen data under a 20-returned image system. During the model development, well-trained ResNet34 are outstanding and no performance difference when comparing five data miners that showed up to 98% in its precision even after testing the model with both image sources: stereomicroscope and mobile phone cameras. The robustness of the proposed—trained model was tested with secondary unseen data which showed different environmental factors such as lighting, image scales, background colors and zoom levels. Nevertheless, our proposed neural network still has great performance with greater than 95% for sensitivity and precision, respectively. Also, the area under the ROC curve given the learning system seems to be practical and empirical with its value greater than 0.960. The results of the study may be used by public health authorities to locate mosquito vectors nearby. If used in the field, our research tool in particular is believed to accurately represent a real-world scenario.

A novel approach for identification of zoonotic trypanosome utilizing deep metric learning and vector database-based image retrieval system

“…Additionally, the requirement for fine-tuning in the CBIR protocol may arise, considering that the number of returned images influences the evaluation metrics [ 43 , 50 ]. Although the kNN value of 10 resulted in the average AUC reaching its peak when comparing to the other values [ 20 ], it is noteworthy that employing kNN = 20 in our study yielded comparable results to those reported by Kittichai et al [ 44 ] and represent a soft-voting approach. Nevertheless, it is important to overcome a significant limitation in the CBIR work must be overcome.…”

“…The utilization of DML in automatically identifying Trypanosoma species yields findings line with prior research. One such study employed DML to distinguish between mosquito vector species in Thailand [ 44 ]. Another investigation focused on classifying lung nodules, employing a two-step content-based image retrieval approach that utilized texture features and a learned distance metric [ 20 ].…”

“…Sensitivity and precision levels surpassing 90 % and 95 %, respectively, were achieved. This incorporation also enhances clustering analysis and provides a framework for predicting region-specific mosquito species [ 44 ].…”

Wing Interferential Patterns (WIPs) and machine learning for the classification of some Aedes species of medical interest

“…Models based on insect morphology imaging of immobilized insects, an approach close to the entomological expertise deployed to identify insects that have inter-genus inter-species high morphological similarities, require a considerable number of data for training each Genus/species to learn the features and gain validation accuracy 10 , 13 , 14 . Databases needed to train such models on whole insect recognition are filled with pictures of several poses, dorsal–ventral, etc., to collect taxonomic discrimant characters 6 , 15 – 17 .…”