ABSTRACT Cervical cancer screening relies on accurate cell classification. Approaches based on Convolutional Neural Networks (CNNs) have proven effective in addressing the task. However, these approaches suffer from two main challenges. First, they may introduce bias into models due to variations in cell morphology and color. Second, they may struggle to capture broader contextual information as CNNs primarily focus on local pixel information. To address these issues, we present a novel hybrid model named DualBran…