Lung cancer is the leading cause of patient mortality in the world. Early diagnosis of malignant pulmonary nodules in CT images can have a significant impact on reducing disease mortality and morbidity. In this work, we propose LMLCC-Net, a novel deep learning framework for classifying nodules from CT scan images using a 3D CNN, considering Hounsfield Unit (HU)-based intensity filtering. Benign and malignant nodules have significant differences in their intensity profile of HU, which was not exploited in the literature. Our method considers the intensity pattern as well as the texture for the prediction of malignancies. LMLCC-Net extracts features from multiple branches that each use a separate learnable HU-based intensity filtering stage. Various combinations of branches and learnable ranges of filters were explored to finally produce the best-performing model. In addition, we propose a semi-supervised learning scheme for labeling ambiguous cases and also developed a lightweight model to classify the nodules. The experimental evaluations are carried out on the LUNA16 dataset. The proposed LMLCC-Net was evaluated using the LUNA16 dataset. Our proposed method achieves a classification accuracy of 91.96%, a sensitivity of 92.94%, and an area under the curve of 94.07%, showing improved performance compared to existing methods The proposed method can have a significant impact in helping radiologists in the classification of pulmonary nodules and improving patient care.

翻译：肺癌是全球患者死亡的主要原因。在CT图像中早期诊断恶性肺结节对降低疾病死亡率和发病率具有显著影响。本研究提出LMLCC-Net，一种新颖的深度学习框架，采用3D CNN结合亨氏单位（HU）强度滤波技术对CT扫描图像中的结节进行分类。良性与恶性结节在HU强度分布上存在显著差异，而现有文献尚未充分利用这一特征。我们的方法同时考虑强度模式和纹理特征进行恶性预测。LMLCC-Net通过多分支架构提取特征，每个分支均采用独立可学习的HU强度滤波层。通过探索不同分支组合与滤波器可学习范围，最终构建出性能最优的模型。此外，我们提出半监督学习方案用于标注模糊病例，并开发了轻量化模型进行结节分类。实验评估在LUNA16数据集上进行。所提出的LMLCC-Net在LUNA16数据集上实现了91.96%的分类准确率、92.94%的敏感度以及94.07%的曲线下面积，性能优于现有方法。该方法有望显著辅助放射科医师进行肺结节分类，从而改善患者诊疗水平。