Artificial intelligence in recognition and classification of astrophysical and medical images

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Over the past decade, digital imaging has advanced significantly across various fields, including medicine, pharmacy, chemistry, biology, astrophysics, meteorology, and geophysics. The surge in digitized images has created a demand for specialized processing techniques and knowledge extraction, often requiring minimal human intervention. This has led to the emergence of pattern recognition, a crucial area in digital processing due to the increasing volume of images. Initially, pattern recognition techniques were applied to medical X-rays and MMR images, enabling the extraction of quantified information related to texture, intensity, and shape, thus enhancing the diagnosis of human organs. Recent developments have integrated feature detection and classification using region-based and artificial intelligence methods. By leveraging extensive databases of medical images processed through these techniques, rapid and consistent disease diagnoses can be achieved based on insights from numerous similar cases. Additionally, CCD cameras in astrophysical instruments, both ground-based and in space, have generated vast amounts of digitized images, contributing to our understanding of the night skies and the Sun, our closest star.