Chromenet: a CNN architecture with comparison of optimizers for classification of human chromosome images

• Published in: Multidimensional systems and signal processing Vol. 33; no. 3; pp. 747 - 768
• Main Authors: Menaka, D., Vaidyanathan, S. Ganesh
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2022; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Birth defects; Chromosomes; Circuits and Systems; Classification; Computer vision; Deep learning; Electrical Engineering; Engineering; Experimentation; Image classification; Image segmentation; Machine learning; Performance measurement; Signal,Image and Speech Processing; Convolution neural network; Karyotyping; Chromosome images; Optimizers; Image classification

Chromosome karyotyping is pivotal in the diagnosis of many genetic disorders and birth defects. Manual karyotyping is a tedious procedure and various techniques were proposed in the literature to automate this process. As Artificial Intelligence algorithms are unfolding their vast potential in a ple...