# A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models
###### Abstract
This methodology sharpens the clarity of the tumor's spatial configuration and provides crucial information that aids effective medical decision-making and treatment planning [4]. Through this research, we aim to showcase the application of deep learning in medical imaging and its potential to transform early detection and treatment. A variety of methods have been developed for lung segmentation, which includes the use of hand-selected features and the careful tuning of experimental parameters [5]. However, these methods are often explicitly tailored for particular applications and datasets, limiting their generalizability, especially in medical imaging data like CT scans. This limitation is further exacerbated by the tendency to overlook certain features during the feature selection phase [6; 7], as practitioners often skip detailed feature selection in favor of more intuitive interactive methods. Traditional machine learning approaches typically adopt a two-step process, beginning with feature selection and then applying a learning algorithm to build an automated system. Initially, experts choose or develop a method specifically for feature selection tailored to the task at hand [8]. In contrast, deep learning models integrate feature selection with the learning process, eliminating the need for separate feature selection algorithms [9; 10; 11; 12]. Deep learning has been particularly effective in lung segmentation, focusing on classifying each pixel as either lung tissue or not. However, this pixel-wise classification approach is challenged by class imbalance, as the number of non-lung pixels significantly outnumbers lung pixels. To address the class imbalance issue, strategies are employed at both the data handling and algorithmic levels [13; 14]. Regarding data handling, techniques include downsampling the overrepresented classes, upsampling the underrepresented classes, or generating synthetic data to balance the dataset. At the algorithmic level, efforts focus on adjusting algorithms to recognize better and weigh the contributions of the less represented classes, which is essential for accurate predictions [15]. The primary problem with data-driven methods lies in their potential to reflect and amplify existing biases in the data, leading to a preference for more common outcomes and overlooking rare but essential cases [16]. From an algorithmic standpoint, the challenge is to adapt learning models to capture these vital, albeit infrequent, phenomena, thereby improving the accuracy and reliability of the model. The advent of RL has garnered considerable attention for its capability to address complex challenges, especially in scenarios marked by class imbalances [17]. RL enhances the efficacy of classification models by filtering out irrelevant details and emphasizing crucial features, demonstrating versatility across various domains. A notable strength of RL is its adaptive learning approach, guided by reward mechanisms, which is particularly effective in managing data imbalance issues [18]. By tailoring the reward system to prioritize accurately identifying underrepresented classes, RL strategies can focus more on these often-neglected segments. This method promotes a more equitable distribution of attention across different courses, significantly improving detection accuracy [19]. The 3D reconstruction from CT images has been thoroughly explored, significantly relying on statistical methodologies [20; 21; 22]. While these approaches offer considerable value, they encounter several notable limitations. One of the primary challenges is the dependency on extensive annotated datasets, which are notably difficult to obtain in medical imaging due to stringent privacy regulations and the necessity for annotations by medical experts. Additionally, these methods have a heightened risk of overfitting, compromising their ability to be applied effectively across varied medical datasets [23].