Explainable artificial intelligence with pyramid vision transformer model for multi-class malignant cell classification on cytology slides

Digital pathology involves the digitisation of histology slides, which is vital in modern healthcare, especially for cancer detection and diagnosis. Still, the manual analysis of these digital slides by expert pathologists is time-consuming and prone to inconsistencies. This workload often results in subjective grading and a lack of agreement between different pathologists (inter-operator variability) and even with the same pathologist over time (intra-operator variability). With the advent of whole slide imaging (WSI) and high computational ability, the application of deep learning (DL), particularly convolutional neural networks, has developed rapidly in the past decades in the domain of digital pathology. Therefore, this study introduces a Hybrid Temporal Deep Learning Network for Automated Malignant Cell Classification in Cytology Slides (HTDL-MCCCS) model. The objective is to improve diagnostic consistency, reduce workload, and enable scalable screening in resource-limited clinical settings. The HTDL-MCCCS model consists of four integrated stages: First, slide pre-processing is performed using stain normalisation, artefact removal, patch extraction, and nuclei segmentation to enhance morphological clarity. Second, the pyramid vision transformer (PVT) is utilised to learn robust cell-level features from unlabeled slide patches. Third, the bidirectional temporal recurrent unit (BiTRU) is employed for the automated classification of malignant and benign cells. Lastly, a detailed analysis using Grad-CAM is included to support clinical interpretability and trust. A wide-ranging simulation analysis of the HTDL-MCCCS methodology portrayed superior outcomes of 98.16% and 96.42% under the SIPaKMeD and Herlev datasets.