改进残差注意力网络，精准识别肺结肠癌病理图像
MRANet: A Modified Residual Attention Networks for Lung and Colon Cancer Classification
- 基于改进残差注意力结构，提升病理图像特征提取能力
- 在25000张图像上实现最高99.3%分类准确率
- 适合医学影像分析与AI辅助诊断场景
肺癌和结肠癌是导致癌症死亡的主要原因。早期准确诊断对有效治疗至关重要。利用不同成像技术的图像检测，学习模型在从组织病理图像自动分类癌症方面展现出巨大潜力，这包括病理诊断这一癌症类型识别的关键因素。本研究致力于构建一种高效深度学习模型，用于从组织病理图像中识别肺癌和结肠癌。我们提出了一种基于改进残差注意力网络架构的新方法。该模型在包含25,000张高分辨率组织病理图像的多类别数据集上进行训练。所提模型在二分类、三分类和五分类任务中分别达到99.30%、96.63%和97.56%的优异准确率，优于其他先进架构。本研究展示了一种高精度的深度学习模型，可有效用于肺癌和结肠癌分类，其卓越性能满足了医疗AI应用中的关键需求。
原文摘要 · Abstract (English)
Lung and colon cancers are predominant contributors to cancer mortality. Early and accurate diagnosis is crucial for effective treatment. By utilizing imaging technology in different image detection, learning models have shown promise in automating cancer classification from histopathological images. This includes the histopathological diagnosis, an important factor in cancer type identification. This research focuses on creating a high-efficiency deep-learning model for identifying lung and colon cancer from histopathological images. We proposed a novel approach based on a modified residual attention network architecture. The model was trained on a dataset of 25,000 high-resolution histopathological images across several classes. Our proposed model achieved an exceptional accuracy of 99.30%, 96.63%, and 97.56% for two, three, and five classes, respectively; those are outperforming other state-of-the-art architectures. This study presents a highly accurate deep learning model for lung and colon cancer classification. The superior performance of our proposed model addresses a critical need in medical AI applications.
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