## 1 Introduction
In Branch 2, convolutional neural network (CNN)-based models are prepared with EBUS images. Lastly, multi-patch EBUS images are selected from the original EBUS images to train the multi-patch-based model in Branch 3. Thus, three models are aggregated using the weighted ensemble strategy. [PERSON] et al. [4] proposed a CAD system that automatically differentiates benign and malignant PPLs for early lung cancer detection with EBUS images. Notably, this CAD system incorporates the transformer learning technique, and rotation and flipping augmentation methods are also included in the training dataset to avoid overfitting. CNN models extract features, which the SVM then classifies. [PERSON] et al. [9] also designed a computer-aided classification method to differentiate lung cancer using CT images from an artificial neural network (ANN). Statistical parameters, such as mean, standard deviation, skewness, kurtosis, fifth central moment, and sixth central moment, are used for classification. [PERSON] et al. [10] introduced a CAD system for lung cancer classification utilizing CT scans and a dataset from the 2017 Kaggle Data Science Bowl. In this two-stage system, a three-dimensional convolutional neural network (3D-CNN) classifies CT scans as lung cancer positive or negative. First, the modified U-Net detects nodule candidates in CT images of segmented lungs. Then, the regions where most nodule candidates are likely located are fed into a 3D-CNN to provide a final classification result. Alternatively, [PERSON] et al. [11] employed a radiomics methodology to predict the histology of non-small cell lung cancer tumors using standard-of-care computed tomography (CT) data. Moreover, a comparable discriminative performance yielded reliable results by implementing machine learning classifiers such as k-nearest neighbor and SVM for CNN-derived quantitative radiomics features. Their best-performing CNNs functioned as effective probabilistic classifiers in a heterogeneous test set, providing qualitatively interpretable prediction visuals and proving that deep learning-based radiomics can identify histological lung cancer phenotypes. However, early-stage benign and malignant nodules' remarkable similarity hinders accurate detection and classification. Therefore, [PERSON] et al. [12] developed an automated framework for detecting and classifying lung nodules by utilizing two deep 3D customized mixed-link network (CMixNet) architectures. This framework aims to address the challenges associated with lung nodule analysis. For the nodule detection task, the faster R-CNN algorithm is employed to leverage efficiently-learned features obtained from the CMixNet architecture and a U-Net-like encoder-decoder architecture. On the other hand, the nodule classification task utilizes a gradient boosting machine (GBM) that operates on the learned features extracted from the designed 3D CMixNet structure. To minimize false positives and misdiagnosed results stemming from various error types, the final decision incorporates physiological symptoms and clinical biomarkers. [PERSON] et al. [13] conducted a study focusing on the diagnosis of lung cancer using deep attention-based multiple-instance learning (MIL) and radiomics features extracted from CT scan images. Their research approach treated the task of lung cancer diagnosis as a multiple-instance learning problem; radiomics features were selected as the input features source, and deep attention-based MIL was responsible for classification. The attention mechanism improved interpretability by estimating each instance's value for the final diagnosis set. Although CAD systems have proven remarkable efficacy in lung cancer diagnosis, an ongoing challenge is the need for balanced datasets to obtain satisfactory results with most existing techniques. Recognizing that an imbalanced dataset can significantly impact a machine learning model's performance and accuracy is crucial.