增强组织病理学：通过深度学习与集成技术提升结肠癌检测

Augmented histopathology: Enhancing colon cancer detection through deep learning and ensemble techniques

DOI 原文链接

用sci-hub下载0

MICROSCOPY RESEARCH AND TECHNIQUE

影响因子:2.1

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

DOI: 10.1002/jemt.24692

keywords: attention mechanisms; clinical significance; colon cancer; computational pathology; cross transformers; deep learning models; ensemble learning; feature extraction; histopathological images; multi‐class classification; siamese networks

摘要

结肠癌对人类生命构成重大威胁，具有高全球死亡率。早期且准确的检测对于改善治疗质量和提高生存率至关重要。本文提出了一种全面的方法，以增强结肠癌的检测和分类。组织病理学图像采自CRC-VAL-HE-7K数据集，经过预处理以提升质量，然后通过增强数据扩大数据集规模并增强模型的泛化能力。设计了一种基于深度学习的变换器模型，用于高效特征提取，并结合卷积神经网络（CNN）以增强分类效果。采用交叉变换模型捕获区域之间的长距离依赖，注意机制赋予权重以突出关键特征。为了提升分类准确率，利用Siamese网络根据概率区分不同的结肠癌组织类别。通过优化算法微调模型参数，将结肠癌组织划分为不同类别。在实验评估中，评估了多类别分类性能，结果表明所提模型达到最高准确率98.84%。本研究方法在所有分析中优于其他现有方法。研究亮点包括：提出基于深度学习的技术，利用DL方法提升结肠癌检测与分类，采用CRC-VAL-HE-7K数据集优化图像质量，结合粒子群优化（PSO）和矮 mongoose优化（DMO）进行模型调优，采用PSO-DMO算法微调深度学习模型。

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.