A pyramid convolutional mixer for cervical pap-smear image classification tasks

Convolutional Neural Networks (CNNs) have exhibited considerable success in the realm of cervical cytopathology image classification, owing to their efficient design. We find that existing CNN-based cervical cytopathology classification methods fail to fully exploit the cell morphology and nucleus information. To address the above problems, we propose an efficient network called Pyramid Convolutional Mixer. We capture multi-scale subtle morphology features at the cellular level and convey nuclear neighborhood spatial information by integrating convolutional operations within the transformer structure. PCMixer contains two key modules, i.e. pyramid morphology module (PMM) and nuclear spatial mixing block (NSMB) to retrieve cervical cytopathology information. PMM is characterized by a multi-scale pyramid architecture employing a convolutional layer and a local encoder to generate local morphology information at each scale. In addition, NSMB operates on the input patches to separate the mixing of spatial and channel dimensions to encode nuclear neighborhood spatial information. We intend to unveil a more intricate cervical cytopathology dataset: Cervical Cytopathology Image Dataset (CCID). We achieve a classification accuracy of 89.62% along with precision, recall and F1 score of 82.76%, 85.97% and 84.15% respectively on the CCID dataset. Also, we use cervical cytopathology images from the publicly available SIPaKMeD dataset. We obtain 96.21%, 95.70% 95.60% and 95.30% respectively for the four metrics. Through comprehensive experiments conducted on two real-world datasets, our proposed model demonstrates superior performance compared to state-of-the-art cervical cytopathology classification models. The results demonstrate that our method can significantly assist cytopathologists in appropriately evaluating cervical smears.