Convolutional neural networks are deep neural networks applied to analyze visual imagery. They are mainly known for their ability to recognize patterns and structures in images, making them integral to image and video recognition, recommender systems, and image classification. The brain’s visual cortex organization inspires the architecture of a convolutional Neural Network. It specializes in processing data, such as images, with a grid-like topology. A CNN is composed of different layers: convolutional, pooling, and fully connected layers, which apply a convolution operation to the input and pass it to the next layer. This process enables the network to work efficiently and understand the spatial hierarchies of features, starting from basic edges to more advanced concepts. The pooling layers shrink the spatial dimensions of the representation, which reduces the number of parameters and computations required in the network. Finally, fully connected layers combine all features for the final classification or regression task. CNNs have been instrumental in computer vision, significantly improving the state-of-the-art for tasks like image classification, object detection, and more. Convolutional neural networks (CNNs) play a transformative role in healthcare by enabling advanced medical image analysis, such as in radiology, to detect anomalies in X-rays and MRIs with high accuracy. They also assist in automating diagnostic processes, improving efficiency and accuracy in identifying diseases like cancer, diabetic retinopathy, and other conditions visible in medical imaging.