2.3.1. Convolutional Neural Network (CNN)

A CNN was used only for mounting detection purposes; therefore, only the estrus dataset consisting of images of mounting or non-mounting situations was used, and the images were collected from the internet. Artificial neural networks are models that are based on the functioning of the human brain. The goal of this structure is to perform the learning process, interpret the acquired knowledge, and make decisions autonomously. Convolutional neural networks (CNNs) are a type of artificial neural network that are used primarily for image recognition and computer vision tasks, although they can also be used for other types of data processing, such as natural language processing. CNNs have revolutionized the field of computer vision and continue to be an active area of research and development [38,39]. Computers must recognize and convert incoming images into a computationally manageable matrix format. The first layer in a CNN is a convolutional layer, which applies a set of filters to the input image to extract features, such as edges and corners. The output of the convolutional layer is then passed through an activation function to introduce non-linearity into the model. The output of the activation function is then passed through a pooling layer, which reduces the dimensionality of the feature maps while retaining the most important information. The final output of the network is typically a fully connected layer that performs classification. It learns the impact of these differences on the label during the training phase and then uses this knowledge to make predictions for new images. In this study, a 9-layer convolutional neural network was used, as seen in Figure 5, and the network was trained for 20 epochs with binary classification.

Proposed CNN architecture.