增强组织病理学：通过深度学习和合奏技术增强结肠癌检测
Augmented histopathology: Enhancing colon cancer detection through deep learning and ensemble techniques
影响因子:2.10000
分区:工程技术3区 / 显微镜技术2区 解剖学与形态学3区 生物学4区
发表日期:2025 Jan
作者:
J Gowthamy, S S Subashka Ramesh
摘要
结肠癌对全球死亡率高的人类生命构成了重大威胁。早期和准确的检测对于提高治疗质量和存活率至关重要。本文提出了一种综合的方法来增强结肠癌检测和分类。组织病理学图像是从CRC-VAL-HE-7K数据集收集的。这些图像经历了预处理以提高质量，然后进行增强以增加数据集大小并增强模型概括。基于深度学习的变压器模型设计用于通过合并卷积神经网络（CNN）来有效提取和增强分类。跨转化模型捕获了区域之间的长距离依赖性，而注意机制则分配了权重以突出关键特征。为了提高分类准确性，暹罗网络可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类别。在实验评估中评估了多类分类性能，该绩效表明所提出的模型提供了98.84％的最高准确率。在这篇研究文章中，提出的方法通过与其他现有方法进行比较，在所有分析中都取得了更好的性能。研究重点：提出了基于深度学习的技术。 DL方法用于增强结肠癌检测和分类。 CRC-VAL-HE-7K数据集用于增强图像质量。使用杂交粒子群优化（PSO）和矮杂种杂种优化（DMO）。深度学习模型是通过实现PSO-DMO算法来调整的。
Abstract
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.