FSL-DSC: A Hybrid Pap Smear Cervical Cancer Image Classification Framework Using Few-shot Learning with Depthwise Separable Convolutions

Cervical cancer poses a significant threat to the health of women worldwide. Cervical cytopathology screening is an effective method for diagnosing cervical cancer. However, manual screening is time-consuming and prone to errors. The advent of automatic Computer-Aided Diagnosis (CAD) systems based on deep learning addresses this problem, however training these models requires large amounts of labeled data, which may not always be available. This paper proposes a Few-Shot Learning (FSL) framework called FSL-DSC to perform cervical cell classification tasks on small dataset. FSL-DSC first proposes inner loop learning and outer loop learning for individual tasks and overall parameter updates respectively, then a depthwise separable module is designed to further enhance the performance of the model. Among three repeated experiments, the FSL-DSC framework achieves an average accuracy of 83.34%, which shows the effectiveness and potential of the proposed FSL-DSC in the field of cervical image classification and few-shot tasks.