Chromosome Analysis

Chromosome analysis, crucial for diagnosing genetic disorders and assessing radiation exposure, is undergoing rapid automation through advancements in image processing and deep learning. Current research focuses on developing algorithms, such as convolutional neural networks (CNNs), vision transformers (ViTs), and variational autoencoders (VAEs), to accurately segment, straighten, and classify chromosomes from microscopic images, overcoming challenges posed by curved or overlapping structures. These automated methods aim to improve the speed, accuracy, and accessibility of karyotyping, ultimately enhancing the efficiency of genetic diagnostics and personalized medicine.