Transformer-based deep learning models for lung cancer detection and classification: a comprehensive review

Abstract

Lung cancer is one of the most aggressive and life-threatening diseases worldwide, responsible for a large percentage of cancer-related deaths each year. Early and accurate diagnosis plays a crucial role in improving patient outcomes and reducing mortality rates. However, traditional diagnostic approaches often fail to detect tumors in their initial stages, leading to delayed treatment. Recent developments in deep learning have introduced transformer-based models that demonstrate remarkable capability in analyzing medical images. These models effectively capture spatial and contextual information from medical imaging, enabling better detection, segmentation, and classification of lung tumors. This review presents a detailed examination of transformer-based architectures applied to lung cancer detection and classification. The review summarizes findings from recently published studies to examine current transformer-based approaches, their applications, and emerging research trends. It discusses their performance, strengths, and limitations compared with earlier methods and explores key challenges such as limited labeled data, dataset imbalance, computational complexity, and lack of interpretability. The reviewed studies show that transformer-based models deliver promising performance, although their results vary depending on the dataset, validation method, and model design. The paper also highlights potential future directions, including model optimization, data standardization, and validation across diverse clinical environments, to enhance the reliability and integration of transformer-based models in healthcare applications.