Video-Assisted thoracoscopic surgery (VATS) is a minimally invasive camera-guided surgical technique that enables real-time visualization of the lung through small incisions, minimizing risks and enabling accurate diagnosis or interventions. This procedure plays a vital role in early lung cancer detection, facilitating precise biopsies and cell identification. Given the critical significance of this camera-guided technique, the production of high-quality, distortion-free images is of utmost importance for diagnostic accuracy and effective treatment. Therefore, in order to enhance lung cancer diagnostic accuracy and treatment effectiveness, this paper presents an algorithm for calibrating endoscopic cameras in VATS procedures. Based on Zhang Zhengyou's calibration method and utilizing the OpenCV computer vision library, the algorithm aims to optimize image quality, ensuring a clear and precise visualization. Moreover, this project explores potential grids that can be used within Zhang's method in order to refine camera calibration, emphasizing the critical importance of high-quality, distortion-free images in advancing medical practices.