Simultaneous Identification and Classification of Lung Nodules in CT Images - A Hierarchical Network Approach

With ongoing advancements in Machine Learning (ML) algorithms like Convolutional Neural Networks (CNNs), their effectiveness in medical image analysis, particularly for detecting lung diseases in Computed Tomography (CT) scans, continues to improve. Although early lung nodule detection is vital, many state-of-the-art (SOTA) methods do not support simultaneous identification and classification. This separation can lead to inefficiencies and delays in clinical workflows, especially when real-time diagnosis is required. A unified approach that integrates both tasks is therefore highly desirable. Hierarchical classification models can address this by handling multi-step tasks and utilizing hierarchical label structures. This work proposes a hierarchical image classification approach for lung CT images, capable of distinguishing between nodule and non-nodule cases in the first stage and further classifying detected nodules into adenocarcinoma, small cell carcinoma, large cell carcinoma, and squamous cell carcinoma cases in the second stage. Key contributions include a method for the creation of a custom hierarchical dataset by merging the LIDC-IDRI and the Lung-PET-CT-Dx and the development of a two-level hierarchical classification algorithm with independently optimized subclassifiers. The results of this work demonstrate the effectiveness of the hierarchical model. When evaluating for simultaneous nodule identification and classification, the proposed approach achieves a weighted accuracy of 96.76%, surpassing the performance of separately executed steps. Furthermore, compared to a fat classification model, which achieves a weighted accuracy of 94.40%, the hierarchical approach outperforms it by 2.30 percentage points, highlighting the advantages of utilizing hierarchical label structures.