EfficientNet Algorithm

EfficientNet is a family of convolutional neural network architectures designed for efficient and accurate image classification, achieving high performance with relatively low computational cost. Current research focuses on optimizing EfficientNet for various applications, including medical image analysis (e.g., cancer detection, brain lesion classification), remote sensing (e.g., change detection, building extraction), and other domains like autonomous driving and food classification, often incorporating it into hybrid models or adapting it for specific data types (e.g., 3D images). This efficiency makes EfficientNet a valuable tool for deploying deep learning models on resource-constrained devices and improving the speed and accuracy of diverse applications.