Convolutional Neural Networks (CNNs) have gained significant attention in various domains, including radiology, due to their ability to automatically and adaptively learn spatial hierarchies of features through backpropagation. This review article provides an overview of CNNs, their basic concepts, and their applications in radiology. It discusses the challenges of applying CNNs to radiological tasks, such as small datasets and overfitting, and explores techniques to mitigate these issues. The article highlights the importance of understanding the concepts, advantages, and limitations of CNNs to leverage their potential in diagnostic radiology, ultimately aiming to enhance radiologist performance and improve patient care. Key points include the composition of CNNs, their training process, and the differences between CNNs and other methods used in radiomics. The article also covers specific applications of CNNs in radiology, such as classification, segmentation, detection, and image reconstruction, and discusses future directions and challenges, including the need for large annotated datasets and addressing the black-box nature of deep learning models.Convolutional Neural Networks (CNNs) have gained significant attention in various domains, including radiology, due to their ability to automatically and adaptively learn spatial hierarchies of features through backpropagation. This review article provides an overview of CNNs, their basic concepts, and their applications in radiology. It discusses the challenges of applying CNNs to radiological tasks, such as small datasets and overfitting, and explores techniques to mitigate these issues. The article highlights the importance of understanding the concepts, advantages, and limitations of CNNs to leverage their potential in diagnostic radiology, ultimately aiming to enhance radiologist performance and improve patient care. Key points include the composition of CNNs, their training process, and the differences between CNNs and other methods used in radiomics. The article also covers specific applications of CNNs in radiology, such as classification, segmentation, detection, and image reconstruction, and discusses future directions and challenges, including the need for large annotated datasets and addressing the black-box nature of deep learning models.