# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
###### Abstract  
Lung nodule classification is crucial for the diagnosis and treatment of lung diseases. However, selecting appropriate metrics to evaluate classifier performance is challenging, due to the prevalence of negative samples over positive ones, resulting in imbalanced datasets. This imbalance often necessitates the augmentation of positive samples to train powerful models effectively. Furthermore, specific medical tasks require tailored augmentation methods, the effectiveness of which merits further exploration based on task objectives. This study conducted a detailed analysis of commonly used metrics in lung nodule detection, examining their characteristics and selecting suitable metrics based on this analysis and our experimental findings. The selected metrics were then applied to assessing different combinations of image augmentation techniques for nodule classification. Ultimately, the most effective metric was identified, leading to the determination of the most advantageous augmentation method combinations. Keywords:data augmentation; pulmonary nodule; F-score; image classification +
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models excel in autonomously extracting features through training. Key methodologies in deep learning for feature extraction include Autoencoders, Deep Belief Networks, Deep Boltzmann Machines, and CNN. Computer-Aided Diagnosis (CAD) systems, particularly those targeting the detection of pulmonary nodules, have become increasingly significant, due to their role in the early detection of lung cancer--a leading cause of cancer-related deaths. Effective early detection through CAD models is pivotal, as it considerably improves patient prognosis by identifying potentially treatable lung cancer at an early stage. However, the challenges of analyzing lung nodules and their surrounding tissues are amplified by the limited size and variability of the existing datasets, which often compromise the generalization capabilities of deep learning models, leading to unreliable predictions and limiting practical application. The complexity is increased by CT images being 3D grayscale, where lesions typically exhibit low contrast against normal tissues, making diverse feature extraction particularly challenging. Additionally, the small sizes of lesion areas relative to normal tissues often results in the neglect of high-level feature maps by deep learning models. The prevalence of false nodules in datasets further exacerbates the imbalance, potentially causing models to overfit specific samples rather than generalize effectively. Extensive research has focused on augmentation techniques to address overfitting in CAD applications, enhancing classifiers' generalization and robustness by expanding training datasets. Yet, no single solution fits all; specific augmentation methods need to be tailored to the unique requirements of different models and datasets. In this study, a 3D Convolutional Neural Network (CNN) was employed to classify pulmonary nodules, assessing candidates for indications of disease. Quality metrics are essential for evaluating classifier performance, providing a means to measure and enhance the effectiveness of classification models. While numerous metrics are available for model assessment, the challenge lies in selecting those that align best with the distinct characteristics of various models and datasets. Such careful selection ensures that performance evaluations are accurate and contextually relevant, which is crucial for advancing medical imaging and improving diagnostic tools. Our research included a rigorous comparison of prevalent metrics for lung nodule classification, supplemented by a comprehensive investigation of augmentation techniques, categorized into geometric and photometric methods, applied during the training of classification models. Our findings highlight geometric augmentation methods as particularly beneficial in enhancing model performance.