Convolutional neural networks (CNNs) are a type of deep learning model that has become dominant in various computer vision tasks and are gaining interest in radiology. CNNs are designed to automatically and adaptively learn spatial hierarchies of features through backpropagation using building blocks such as convolution, pooling, and fully connected layers. This review discusses the basic concepts of CNNs and their application in radiology, as well as challenges and future directions. Two main challenges in applying CNNs to radiological tasks are small dataset size and overfitting, along with techniques to minimize them. Understanding the concepts, advantages, and limitations of CNNs is essential to leverage their potential in diagnostic radiology to improve radiologist performance and patient care. CNNs are composed of multiple building blocks, including convolution, pooling, and fully connected layers. Convolution layers perform feature extraction, while pooling layers reduce the dimensionality of the feature maps. Fully connected layers map the extracted features into final outputs. The process of optimizing parameters such as kernels is called training, which is performed to minimize the difference between outputs and ground truth labels through backpropagation and gradient descent. CNNs differ from traditional radiomics methods in that they do not require hand-crafted feature extraction or segmentation by human experts. They are data-hungry and computationally expensive, often requiring GPUs for training. CNNs are effective in processing grid-patterned data such as images, and they can be applied to 2D, 3D, or other types of data. The convolution layer is a fundamental component of CNNs that performs feature extraction through a combination of linear and nonlinear operations. The pooling layer reduces the in-plane dimensionality of the feature maps, introducing translation invariance. The fully connected layer maps the extracted features into final outputs. The last layer activation function is typically different from others and is chosen based on the task, such as softmax for multiclass classification. Training a CNN involves finding kernels and weights that minimize the difference between output predictions and ground truth labels. Backpropagation and gradient descent are used for optimization. The loss function measures the compatibility between output predictions and ground truth labels. Gradient descent is used to iteratively update the learnable parameters to minimize the loss. Overfitting is a challenge in machine learning where a model learns statistical regularities specific to the training set. Techniques such as data augmentation, regularization, batch normalization, and reducing architectural complexity are used to mitigate overfitting. Transfer learning is a common strategy to train a model on a small dataset by using a pretrained network on a large dataset. In radiology, CNNs are applied for classification, segmentation, detection, and other tasks. For classification, CNNs are used to classify lesions into different classes. For segmentation, CNNs are used to identify and delineate organs or anatomical structures. For detection, CNNs are used to identify abnormalities in medical images. In addition, CNNs are used for denoising low-dConvolutional neural networks (CNNs) are a type of deep learning model that has become dominant in various computer vision tasks and are gaining interest in radiology. CNNs are designed to automatically and adaptively learn spatial hierarchies of features through backpropagation using building blocks such as convolution, pooling, and fully connected layers. This review discusses the basic concepts of CNNs and their application in radiology, as well as challenges and future directions. Two main challenges in applying CNNs to radiological tasks are small dataset size and overfitting, along with techniques to minimize them. Understanding the concepts, advantages, and limitations of CNNs is essential to leverage their potential in diagnostic radiology to improve radiologist performance and patient care. CNNs are composed of multiple building blocks, including convolution, pooling, and fully connected layers. Convolution layers perform feature extraction, while pooling layers reduce the dimensionality of the feature maps. Fully connected layers map the extracted features into final outputs. The process of optimizing parameters such as kernels is called training, which is performed to minimize the difference between outputs and ground truth labels through backpropagation and gradient descent. CNNs differ from traditional radiomics methods in that they do not require hand-crafted feature extraction or segmentation by human experts. They are data-hungry and computationally expensive, often requiring GPUs for training. CNNs are effective in processing grid-patterned data such as images, and they can be applied to 2D, 3D, or other types of data. The convolution layer is a fundamental component of CNNs that performs feature extraction through a combination of linear and nonlinear operations. The pooling layer reduces the in-plane dimensionality of the feature maps, introducing translation invariance. The fully connected layer maps the extracted features into final outputs. The last layer activation function is typically different from others and is chosen based on the task, such as softmax for multiclass classification. Training a CNN involves finding kernels and weights that minimize the difference between output predictions and ground truth labels. Backpropagation and gradient descent are used for optimization. The loss function measures the compatibility between output predictions and ground truth labels. Gradient descent is used to iteratively update the learnable parameters to minimize the loss. Overfitting is a challenge in machine learning where a model learns statistical regularities specific to the training set. Techniques such as data augmentation, regularization, batch normalization, and reducing architectural complexity are used to mitigate overfitting. Transfer learning is a common strategy to train a model on a small dataset by using a pretrained network on a large dataset. In radiology, CNNs are applied for classification, segmentation, detection, and other tasks. For classification, CNNs are used to classify lesions into different classes. For segmentation, CNNs are used to identify and delineate organs or anatomical structures. For detection, CNNs are used to identify abnormalities in medical images. In addition, CNNs are used for denoising low-d