Deep Learning-Driven Gender Classification of Drosophila melanogaster: Advancing Real-Time Analysis with Mobile Integration

Abstract

Researchers extensively use Drosophila melanogaster as a model organism in genetic research and developmental studies to better understand human diseases such as cancer and diabetes. For such investigations, it is necessary to identify and classify male and female flies. This study uses deep learning techniques for gender classification of Drosophila melanogaster via models YOLOv8, Detectron2, ResNet-50, InceptionV3, and MobileNet. Benchmarking revealed that YOLOv8 achieved a maximum accuracy of 98.0%. The classification and object detection models are integrated into a mobile application, facilitating real-time gender classification. This study demonstrates the possibility of integrating deep learning models with mobile technology while ensuring an efficient way for gender prediction, leading to better reproducibility and generating new opportunities in biological research.