Binary Image Classification

Binary image classification focuses on automatically assigning one of two labels to an image, a task crucial in various applications like medical diagnosis and optical character recognition. Current research explores diverse approaches, including deep learning models like convolutional neural networks and autoencoders, as well as less computationally intensive methods such as nearest-neighbor classifiers combined with compression algorithms and quantum pre-processing filters. These advancements aim to improve accuracy, efficiency, and robustness, particularly in scenarios with limited data or class imbalance, thereby impacting fields ranging from healthcare to security.