Ayivi Williams, Zhang Xiaoling, Ativi Wisdom Xornam, Sam Francis, Kouassi Franck A P
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Department of Information Systems and Operations Management, Vienna University of Economics and Business, 1020 Vienna, Austria.
J Imaging. 2025 Aug 21;11(8):283. doi: 10.3390/jimaging11080283.
Lung cancer is one of the leading causes of cancer-related mortality worldwide. The diagnosis of this disease remains a challenge due to the subtle and ambiguous nature of early-stage symptoms and imaging findings. Deep learning approaches, specifically Convolutional Neural Networks (CNNs), have significantly advanced medical image analysis. However, conventional architectures such as ResNet50 that rely on first-order pooling often fall short. This study aims to overcome the limitations of CNNs in lung cancer classification by proposing a novel and dynamic model named LungSE-SOP. The model is based on Second-Order Pooling (SOP) and Squeeze-and-Excitation Networks (SENet) within a ResNet50 backbone to improve feature representation and class separation. A novel Dynamic Feature Enhancement (DFE) module is also introduced, which dynamically adjusts the flow of information through SOP and SENet blocks based on learned importance scores. The model was trained using a publicly available IQ-OTH/NCCD lung cancer dataset. The performance of the model was assessed using various metrics, including the accuracy, precision, recall, F1-score, ROC curves, and confidence intervals. For multiclass tumor classification, our model achieved 98.6% accuracy for benign, 98.7% for malignant, and 99.9% for normal cases. Corresponding F1-scores were 99.2%, 99.8%, and 99.9%, respectively, reflecting the model's high precision and recall across all tumor types and its strong potential for clinical deployment.
肺癌是全球癌症相关死亡的主要原因之一。由于早期症状和影像学表现的细微和模糊性质，这种疾病的诊断仍然是一项挑战。深度学习方法，特别是卷积神经网络（CNN），显著推动了医学图像分析的发展。然而，诸如依赖一阶池化的ResNet50等传统架构往往存在不足。本研究旨在通过提出一种名为LungSE - SOP的新颖动态模型来克服CNN在肺癌分类中的局限性。该模型基于ResNet50骨干网络中的二阶池化（SOP）和挤压激励网络（SENet），以改善特征表示和类别分离。还引入了一种新颖的动态特征增强（DFE）模块，它根据学习到的重要性分数动态调整通过SOP和SENet模块的信息流。该模型使用公开可用的IQ - OTH/NCCD肺癌数据集进行训练。使用各种指标评估模型的性能，包括准确率、精确率、召回率、F1分数、ROC曲线和置信区间。对于多类肿瘤分类，我们的模型在良性病例中准确率达到98.6%，恶性病例中达到98.7%，正常病例中达到99.9%。相应的F1分数分别为99.2%、99.8%和99.9%，反映了该模型在所有肿瘤类型中的高精度和召回率以及其在临床应用中的强大潜力。
