结肠癌对人类生命构成重大威胁，全球死亡率很高。早期准确的检测对于提高治疗质量和生存率至关重要。本文提出了一种增强结肠癌检测和分类的综合方法。组织病理学图像是从 CRC-VAL-HE-7K 数据集收集的。图像经过预处理以提高质量，然后进行增强以增加数据集大小并增强模型泛化。基于深度学习的 Transformer 模型旨在通过合并卷积神经网络 (CNN) 来有效提取特征并增强分类。交叉转换模型捕获区域之间的远程依赖关系，注意力机制分配权重以突出关键特征。为了提高分类准确性，连体网络根据概率区分结肠癌组织类别。优化算法微调模型参数，将结肠癌组织分为不同的类别。实验评估中评估了多类分类性能，表明所提出的模型提供了高达98.84%的最高准确率。在这篇研究文章中，通过与其他现有方法相比，所提出的方法在所有分析中都取得了更好的性能。研究亮点：提出了基于深度学习的技术。深度学习方法用于增强结肠癌的检测和分类。利用CRC-VAL-HE-7K数据集来提高图像质量。使用混合粒子群优化（PSO）和矮猫鼬优化（DMO）。深度学习模型通过实施 PSO-DMO 算法进行调整。© 2024 Wiley periodicals LLC。

Colon cancer poses a significant threat to human life with a high global mortality rate. Early and accurate detection is crucial for improving treatment quality and the survival rate. This paper presents a comprehensive approach to enhance colon cancer detection and classification. The histopathological images are gathered from the CRC-VAL-HE-7K dataset. The images undergo preprocessing to improve quality, followed by augmentation to increase dataset size and enhance model generalization. A deep learning based transformer model is designed for efficient feature extraction and enhancing classification by incorporating a convolutional neural network (CNN). A cross-transformation model captures long-range dependencies between regions, and an attention mechanism assigns weights to highlight crucial features. To boost classification accuracy, a Siamese network distinguishes colon cancer tissue classes based on probabilities. Optimization algorithms fine-tune model parameters, categorizing colon cancer tissues into different classes. The multi-class classification performance is evaluated in the experimental evaluation, which demonstrates that the proposed model provided highest accuracy rate of 98.84%. In this research article, the proposed method achieved better performance in all analyses by comparing with other existing methods. RESEARCH HIGHLIGHTS: Deep learning-based techniques are proposed. DL methods are used to enhance colon cancer detection and classification. CRC-VAL-HE-7K dataset is utilized to enhance image quality. Hybrid particle swarm optimization (PSO) and dwarf mongoose optimization (DMO) are used. The deep learning models are tuned by implementing the PSO-DMO algorithm.© 2024 Wiley Periodicals LLC.