Lung cancer is a deadly type of malignancy that poses a significant threat to human health. Accurately identifying the subtypes of lung cancer is critical for effective treatment. However, conventional methods for determining subtypes, such as histological examination, are invasive and time-consuming. In order to overcome this problem, a non-invasive approach for predicting lung cancer subtypes using multi-modality images with a hybrid model is proposed in this study. The model combines attention-based Convolutional Neural Networks (CNNs) and machine learning classifiers to achieve this objective. The model employs a soft-attention mechanism to focus on the pathological areas and extract both global and local features from the images. The stack-based ensemble classifier employs logistic regression as a meta-learner and four machine learning classifiers, including Support Vector Machine (SVM), Naive Bayes, Random Forest, and J48. The classifier categorizes lung cancer into Adenocarcinoma (ADC), Squamous Cell Carcinoma (SQC), and Small Cell Carcinoma (SCC). The suggested model validated using publically accessible datasets (Lung-PET-CT-DX, Lung1, and Lung3) achieved superior performance, with a validation accuracy of 98.8%, an F1-score of 0.986, and a Matthews Correlation Coefficient (MCC) of 0.988.

肺癌是一种致命的恶性肿瘤，对人类健康构成重大威胁。准确识别肺癌的亚型对于有效治疗至关重要。然而，确定亚型的传统方法（例如组织学检查）是侵入性且耗时的。为了克服这个问题，本研究提出了一种利用混合模型的多模态图像来预测肺癌亚型的非侵入性方法。该模型结合了基于注意力的卷积神经网络（CNN）和机器学习分类器来实现这一目标。该模型采用软注意力机制来关注病理区域并从图像中提取全局和局部特征。基于堆栈的集成分类器采用逻辑回归作为元学习器和四个机器学习分类器，包括支持向量机 (SVM)、朴素贝叶斯、随机森林和 J48。该分类器将肺癌分为腺癌 (ADC)、鳞状细胞癌 (SQC) 和小细胞癌 (SCC)。使用可公开访问的数据集（Lung-PET-CT-DX、Lung1 和 Lung3）验证的建议模型取得了优异的性能，验证准确度为 98.8%，F1 分数为 0.986，马修斯相关系数 (MCC) 为0.988。