A hierarchical framework for cervical cell classification using attention-based multi-scale local binary convolutional neural networks

Traditional classification methods for cervical cells heavily rely on manual feature extraction, constraining their versatility due to the intricacies of cytology images. Although deep learning approaches offer remarkable potential, they often sacrifice domain-specific knowledge, particularly the morphological patterns characterizing various cell subtypes during automated feature extraction. To bridge this gap, we introduce a novel hierarchical framework that integrates robust features from color, texture, and morphology with latent representations discovered by an improved attention-based multi-scale local binary convolutional neural networks (MS-LBCNN), designed to facilitate powerful feature extraction mechanism. We enhance the standard 6-class Bethesda system (TBS) classification by incorporating a coarse-to-refine fusion strategy, which optimizes the classification process. The proposed method is uniquely equipped to manage the complexities present in both individual and clustered cell images. Upon rigorous evaluation across three independent data cohorts, our method consistently surpassed existing state-of-the-art techniques. The experimental results indicated the potential of our method in enhancing the development of automation-aided diagnostic systems, and bolstering both the accuracy and efficiency of cytology screening procedures.