Background: Lung and colon cancers are among the most prevalent and lethal malignancies worldwide, underscoring the urgent need for advanced diagnostic methodologies. This study aims to develop a hybrid deep learning and machine learning framework for the classification of Colon Adenocarcinoma, Colon Benign Tissue, Lung Adenocarcinoma, Lung Benign Tissue, and Lung Squamous Cell Carcinoma from histopathological images. Methods: Current approaches primarily rely on the LC25000 dataset, which, due to image augmentation, lacks the generalizability required for real-time clinical applications. To address this, Contrast Limited Adaptive Histogram Equalization (CLAHE) was applied to enhance image quality, and 1000 new images from the National Cancer Institute GDC Data Portal were introduced into the Colon Adenocarcinoma, Lung Adenocarcinoma, and Lung Squamous Cell Carcinoma classes, replacing augmented images to increase dataset diversity. A hybrid feature extraction model combining MobileNetV2 and EfficientNetB3 was optimized using the Grey Wolf Optimizer (GWO), resulting in the Lung and Colon histopathological classification technique (MEGWO-LCCHC). Cross-validation and hyperparameter tuning with Optuna were performed on various machine learning models, including XGBoost, LightGBM, and CatBoost. Results: The MEGWO-LCCHC technique achieved high classification accuracy, with the lightweight DNN model reaching 94.8%, LightGBM at 93.9%, XGBoost at 93.5%, and CatBoost at 93.3% on the test set. Conclusions: The findings suggest that our approach enhances classification performance and offers improved generalizability for real-world clinical applications. The proposed MEGWO-LCCHC framework shows promise as a robust tool in cancer diagnostics, advancing the application of AI in oncology.
背景：肺癌与结肠癌是全球范围内最常见且致死率最高的恶性肿瘤之一，这凸显了对先进诊断方法的迫切需求。本研究旨在开发一种结合深度学习与机器学习的混合框架，用于从组织病理学图像中分类结肠腺癌、结肠良性组织、肺腺癌、肺良性组织及肺鳞状细胞癌。方法：现有方法主要依赖LC25000数据集，该数据集因图像增强处理而缺乏实时临床应用所需的泛化能力。为解决此问题，本研究采用对比度受限自适应直方图均衡化技术提升图像质量，并从美国国家癌症研究所GDC数据门户引入1000张新图像，分别补充至结肠腺癌、肺腺癌和肺鳞状细胞癌类别，替代增强图像以增加数据集多样性。通过灰狼优化算法对融合MobileNetV2与EfficientNetB3的混合特征提取模型进行优化，构建出肺与结肠组织病理学分类技术。采用Optuna工具对XGBoost、LightGBM、CatBoost等多种机器学习模型进行交叉验证与超参数调优。结果：MEGWO-LCCHC技术取得优异分类性能，测试集上轻量化深度神经网络模型准确率达94.8%，LightGBM为93.9%，XGBoost为93.5%，CatBoost为93.3%。结论：研究结果表明，该方法显著提升了分类性能，并为实际临床应用提供了更好的泛化能力。所提出的MEGWO-LCCHC框架有望成为癌症诊断的可靠工具，推动人工智能在肿瘤学领域的应用发展。
肿瘤(癌症)患者之家