Deep learning in disease vector image identification.

Area of Science:

• Public Health
• Computer Science
• Entomology

Background:

• Vector-borne diseases (VBDs) pose a significant global health risk, affecting 80% of the world's population.
• Manual identification of disease vectors is labor-intensive, requires expertise, and impedes effective disease control strategies.
• Deep learning (DL) offers automated solutions for complex identification tasks, showing promise for VBD management.

Purpose of the Study:

• To comprehensively review the current state of deep learning (DL) applications in disease vector identification.
• To explore DL's potential in automating and improving the accuracy of vector identification processes.
• To identify challenges and future research directions for DL in VBD control.

Main Methods:

• Literature review of studies applying deep learning to disease vector identification.
• Analysis of data collection, preprocessing, and model construction techniques used in DL for vector identification.
• Examination of evaluation metrics and application areas, including species classification, object detection, and breeding site identification.

Main Results:

• Deep learning models demonstrate significant potential for automating disease vector identification across various tasks.
• Current research covers data preparation, model development, and evaluation methodologies for DL in vector identification.
• DL applications range from classifying vector species to detecting them and identifying breeding grounds.

Conclusions:

• Deep learning presents a powerful tool to enhance disease vector identification, thereby improving VBD control efforts.
• Further research is needed to address existing challenges and fully realize the prospects of DL in this field.
• The integration of DL can lead to more efficient and accurate surveillance and management of vector-borne diseases.