Accurate segmentation of cervical nuclei is an essential step in the early diagnosis of cervical cancer. Still, there are few studies on the segmentation of clustered nuclei in clusters of cells. Because of the complexities of high cell overlap, blurred nuclei boundaries, and clustered cells, the accurate segmentation of clustered nuclei remains a pressing challenge. In this paper, we purposefully propose a GCP-Net deep learning network to handle the challenging cervical cluster cell images. The proposed U-Net-based GCP-Net consists of a pretrained ResNet-34 model as encoder, a Gating Context-aware Pooling (GCP) module, and a modified decoder. The GCP module is the primary building block of the network to improve the quality of feature learning. It allows the GCP-Net to refine details of feature maps leveraging multiscale context gating and Global Context Attention for the spatial and texture dependencies. The decoder block including Global Context Attention- (GCA-) Residual Block helps build long-range dependencies and global context interaction in the decoder to refine the predicted masks. We conducted extensive comparative experiments with seven existing models on our ClusteredCell dataset and three typical medical image datasets, respectively. The experimental results showed that the GCP-Net obtained promising results on three evaluation metrics AJI, Dice, and PQ, demonstrating the superiorities and generalizability of our GCP-Net for automatic medical image segmentation in comparison with some SOAT baselines.

The rapid growth of social media has revolutionized the way individuals share and access opinions, generating vast amounts of textual data containing diverse sentiments. Sentiment analysis enables organizations to derive valuable insights from this data, facilitating improved decision-making. However, the limited availability of labeled datasets and challenges related to data imbalance often hinder the development of robust sentiment analysis models. In this study, we propose a novel framework dynamic Easy Data Augmentation (EDA) technique with a Multi-Channel Convolutional Neural Network (MCNN) to address these issues. The EDA techniques including synonym replacement (SR), random insertion (RI), random deletion (RD), and random swap (RS), enrich the training data by introducing diversity, thereby enhancing model robustness. The MCNN architecture effectively captures local patterns in text using multiple filter sizes, enabling comprehensive feature extraction with low computational overhead. Furthermore, the integration of Word2Vec embeddings ensures meaningful text representation, leveraging context-independent word relationships for improved sentiment analysis. Experimental evaluations demonstrate the effectiveness of the proposed approach, highlighting significant improvements in F1-score compared to baseline models. The findings underscore the potential of dynamic text augmentation and efficient deep learning architectures to overcome the challenges of limited training data, offering a scalable solution for sentiment analysis in real-world applications. Additionally, the proposed model achieves an average performance improvement of 11.34% over previous models.