## 1 Introduction  
In accordance with GLOBOCAN 2020 data, which encompasses cancer incidence and mortality estimates compiled by the International Agency for Research on Cancer, the global tally for new cancer cases in 2020 amounted to approximately 19.3 million, with a corresponding death toll of 10.0 million (excluding nonmelanoma skin cancer, the figures stand at 18.1 million new cases and 9.9 million deaths). Among these cases, lung cancer held the second position in terms of incidence, accounting for 11.4% of all reported new cases. However, it claimed the highest number of lives, accounting for 18% of the total deaths [1]. Lung cancer remains the utmost proponent of cancer death worldwide, with a mere 15% overall five-year survival rate. Most cases are diagnosed at an advanced stage when treatment outcomes are not ideal and newly diagnosed patients develop locally advanced or metastatic diseases [2]. Thus, early diagnosis is imperative for propelling lung cancer survival rates, successful treatment, and management. Unfortunately, early lung cancer detection methods are currently insufficient. A thoracic radiograph (TR), commonly known as a chest X-ray (CXR), serves as the primary modality employed in the initial assessment of individuals with suspected lung malignancy. Its widespread accessibility, technical feasibility, minimal risk, and cost-effectiveness contribute to its frequent utilization in this context. The biopsy is another prominent clinical method for lung cancer diagnosis, but taking a biopsy from peripheral pulmonary nodules is notably challenging for physicians. Although computed tomography (CT)-guided percutaneous biopsies are often preferred, pneumothorax, bleeding, and other procedure complications are sometimes problematic. Transbronchial biopsy is customary in Japan, with several steps to ensure a high diagnostic yield: (1) accurately reach the target, (2) confirm the target lesion, (3) collect the biopsy sample from the target, and (4) adequate sampling from the target. In addition, rEBUS technology aids in confirming that the device has reached the target. However, a successful sampling is not always guaranteed because rEBUS is not a real-time guided biopsy procedure. Nonetheless, rEBUS's image analysis will facilitate additional sampling during the biopsy procedure and treatment recommendations. Endobronchial ultrasonography (EBUS) has recently been used to screen for peripheral pulmonary lesions. This radiation-free medical technique uses ultrasound and real-time scanning to diagnose and stage lung diseases, incorporating an endoscope to access the lungs through the airways. It provides high-resolution images for real-time lung tissue and surrounding structure visualizations. EBUS is routinely employed for lung nodule evaluations, lung cancer staging, and various lung disease diagnoses, enabling physicians to make informed decisions regarding treatment options. Clinical studies have attempted to distinguish peripheral pulmonary lesions (PPLs) from EBUS images [3; 4; 5; 6]; however, benign and malignant PPL distinctions are subjective to physician experience and ve even the most experienced physicians. With current artificial intelligence (AI) advancements, AI technology has been increasingly integrated into the medical field. Many computer-aided diagnoses (CAD) have been presented to assist physicians through medical image processing. [PERSON] et al. [3] employed two radiomics features, namely the adaptive weighted sum of the upper triangular gray-level co-occurrence matrix (GLCM) and the adaptive weighted sum of the lower triangular GLCM, to visualize the classifications of peripheral pulmonary lesions using EBUS images. The classification of these lesions was performed using the genetic algorithm in conjunction with a support vector machine (SVM) [7] approach. Within the following year, a three-branch classification framework was proposed for differentiating PPLs. The radiomics feature and patient data are used in Branch 1 to train the random forest classifier [8].