Evolution of Lung Tumor Segmentation: Comprehensive Analysis

Recent advancements in lung tumor detection have leveraged the power of deep learning to enhance the accuracy and efficiency of diagnostic processes. From the early days of radiography to the emergence of computeraided detection systems, the field has evolved to adopt sophisticated machine learning algorithms, with convolutional neural networks (CNNs) at the forefront of this transformation. This review paper examines the historical progression, current methodologies, and challenges within lung tumor detection and segmentation. We discuss the transition from manual feature extraction to the implementation of advanced architectures like U-Net for image segmentation and explore the potential of techniques such as EK Mean Clustering and SVM, Dilated CNN, and RRc-UNET 3D models. The paper identifies scalability, algorithm adaptability, and class imbalance as key challenges and suggests future research directions, including multimodal data fusion, refined model architecture, and the development of interpretable machine learning models for enhanced clinical trust.