Cervical cancer is one of the primary causes of death in women. It should be diagnosed early and treated according to the best medical advice, similar to other diseases, to ensure that its effects are as minimal as possible. Pap smear images are one of the most constructive ways for identifying this type of cancer. This study proposes a cross‐attention‐based Transfomer approach for the reliable classification of cervical cancer in pap smear images. In this study, we propose the CerviFormer‐a model that depends on the Transformers and thereby requires minimal architectural assumptions about the size of the input data. The model uses a cross‐attention technique to repeatedly consolidate the input data into a compact latent Transformer module, which enables it to manage very large‐scale inputs. We evaluated our model on two publicly available pap smear datasets. For 3‐state classification on the Sipakmed data, the model achieved an accuracy of 96.67%. For 2‐state classification on the Herlev data, the model achieved an accuracy of 94.57%. Experimental results on two publicly accessible datasets demonstrate that the proposed method achieves competitive results when compared to contemporary approaches. The proposed method brings forth a comprehensive classification model to detect cervical cancer in pap smear images. This may aid medical professionals in providing better cervical cancer treatment, consequently, enhancing the overall effectiveness of the entire testing process.

中文翻译：

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CerviFormer：一种基于子宫颈抹片检查的宫颈癌分类方法，使用交叉注意力和潜在变压器

宫颈癌是女性死亡的主要原因之一。与其他疾病类似，应尽早诊断并根据最佳医疗建议进行治疗，以确保其影响尽可能小。巴氏涂片图像是识别此类癌症最有建设性的方法之一。本研究提出了一种基于交叉注意力的 Transfomer 方法，用于对巴氏涂片图像中的宫颈癌进行可靠分类。在本研究中，我们提出了 CerviFormer——一种依赖于 Transformer 的模型，因此需要对输入数据大小的最小架构假设。该模型使用交叉注意技术将输入数据重复整合到一个紧凑的潜在 Transformer 模块中，这使其能够管理非常大规模的输入。我们在两个公开可用的巴氏涂片数据集上评估了我们的模型。对于 Sipakmed 数据的三态分类，该模型的准确率达到 96.67%。对于 Herlev 数据的 2 状态分类，该模型的准确率达到 94.57%。两个可公开访问的数据集的实验结果表明，与当代方法相比，所提出的方法取得了有竞争力的结果。该方法提出了一种综合分类模型来检测巴氏涂片图像中的宫颈癌。这可以帮助医疗专业人员提供更好的宫颈癌治疗，从而提高整个检测过程的整体有效性。