Kissing Bugs Identification Using Convolutional Neural Network

• Published in: IEEE access Vol. 9; pp. 140539 - 140548
• Main Authors: Abdelghani, Bassam A., Banitaan, Shadi, Maleki, Mina, Mazen, Amna
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applications programs; Artificial neural networks; Chagas disease; Computer bugs; convolutional neural network; Convolutional neural networks; Deep learning; Diseases; Feature extraction; Insects; kissing bugs; Neural networks; Parasitic diseases; Species classification; Transfer learning
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3119587

Chagas disease is one of the most important parasitic diseases transmitted to animals and people by insect vectors. According to the World Health Organization, around seven million people were infected with Trypanosoma cruzi (also known as kissing bug) that causes Chagas disease. As kissing bugs belong to different families with different danger levels, accurate classifications of kissing bugs species would help the public authorities create a controlled surveillance system. Clinical methods for detecting kissing bugs are expensive, time-consuming, and need a high level of expertise. To overcome these limitations, computational methods can be used. In this paper, a fully automated deep learning model using a convolutional neural network (CNN) with a fine-tuned transfer learning model is proposed to identify kissing versus non-kissing bugs and classify the type of kissing bug species. The accuracy of 99.45% for the classifications of kissing vs. non-kissing bugs and 96% for the classifications of different kissing bugs species is achieved. Finally, a web application is developed based on the proposed model to help the community collecting and identifying kissing bugs species.