Among women, cervical cancer is considered as the most disastrous disease, along with the maximized rate of mortality and illness. In this case, there is a requirement for the earlier detection of cervical tumors to reduce the rate of death. Moreover, it has offered a deep insight into the anatomical details of both normal and abnormal cervix and aided for better treatment in advance. Consequently, the low contrast and the heterogeneity of the biomedical images and the ultra-modern tumor segmentation techniques have faced various limitations of insensitive identification of small lesion regions. Additionally, the traditional categorization techniques have various limitations, such as lesser generalization ability, low accuracy and low efficiency, particularly over complex situations. To conquer such issues, a novel attention-based model is proposed. At first, the source images are fetched from the benchmark data links, which are then undergone for the pre-processing stage. Further, the image segmentation uses Multiscale ResUNet++ with Fuzzy C-means Clustering, where the Region of Interest is segmented separately. Finally, the segmented regions are subjected to the hybrid model as Serial Cascaded Residual Attention with Long Short-Term Memory for severity classification, where some of the hyperparameters are tuned optimally by Hybrid Arithmetic Dolphin Swarm Optimization. The experimentation is done by analyzing the performance with multiple metrics over others. At last, the findings offer that it requires increased classification and segmentation outcomes to diagnose disease effectively.

对于女性来说，宫颈癌被认为是最具灾难性的疾病，死亡率和患病率也是最高的。在这种情况下，就需要尽早发现宫颈肿瘤，以降低死亡率。此外，它还可以深入了解正常和异常子宫颈的解剖细节，并有助于提前更好的治疗。因此，生物医学图像的低对比度和异质性以及超现代的肿瘤分割技术面临着对小病变区域不敏感识别的各种限制。此外，传统的分类技术存在各种局限性，例如泛化能力较差、准确率低、效率低，特别是在复杂情况下。为了克服这些问题，提出了一种新颖的基于注意力的模型。首先，从基准数据链接获取源图像，然后进行预处理阶段。此外，图像分割使用带有模糊 C 均值聚类的多尺度 ResUNet++，其中感兴趣区域被单独分割。最后，将分割区域置于具有长短期记忆的串行级联残留注意力混合模型中进行严重性分类，其中一些超参数通过混合算术海豚群优化进行优化调整。实验是通过使用多个指标分析相对于其他指标的性能来完成的。最后，研究结果表明，需要增加分类和细分结果才能有效诊断疾病。