A self-learning based explainable AI framework for giant crab sex classification

Abstract

Sexual dimorphism is prevalent in crustaceans, and in crabs, it can be observed in body size, carapace shape, and larger and differently shaped claws in males, among other traits. With the advancement of artificial intelligence (AI), automatic sex classification is now possible by leveraging these differential traits between males and females. Traditionally, most research has relied on analysing the entire crab to classify its sex. Subsequently, studies have focused on specific features, such as the abdominal flap or carapace, which facilitate sex differentiation by humans. However, human intervention creates a significant labelling burden and constrains the exploration of other potentially valuable characteristics for research. In this study, a novel framework is introduced for automatic sex classification in crabs. Unlike traditional approaches that rely heavily on manual labelling and predefined features such as abdominal flaps, our framework minimizes human intervention and enables the model to autonomously highlight image regions most informative for classification. This draws attention to underutilized morphological regions that may be useful for sex classification without requiring prior biological knowledge. Furthermore, the framework provides visual explanations of the model’s decisions, enhancing interpretability. Using this approach, we achieved a classification accuracy of 95.4%, while also pinpointing specific regions contributing to the decision-making process.