<p>Cancer is the second leading cause of death worldwide, and within this type of disease, lung cancer is the second most diagnosed, but the leading cause of death. Early detection is crucial to increase patient survival rates. One of the primary methods for detecting this disease is through medical imaging, which, due to its features, is well-suited for analysis by deep learning techniques. These techniques have demonstrated exceptional results in similar tasks. Therefore, this paper focusses on analyzing the latest work related to lung cancer detection using deep learning, providing a clear overview of the state of the art and the most common research directions pursued by researchers. We have reviewed DL techniques for lung cancer detection between 2018 and 2023, analyzing the different datasets that have been used in this domain and providing an analysis between the different investigations. In this state-of-the-art review, we describe the main datasets used in this field and the primary deep learning techniques used to detect radiological signs, predominantly convolutional neural networks (CNNs). As the impact of these systems in medicine can pose risks to patients, we also examine the extent to which explainable AI techniques have been applied to enhance the understanding of these systems, a crucial aspect for their real-world application. Finally, we will discuss the trends that the domain is expected to follow in the coming years and the challenges that researchers will need to address.</p>