A system on chip for automatic karyotyping system. (November 2017)

• Record Type: Journal Article
• Authors: Abid, Faroudja; Hamami, Latifa; Badache, Faiza; Derdour, Houssem
• Is Part Of: Computers & electrical engineering. Volume 64(2017)
• Page Start: 1
• Page End: 14
• Publication Date: 2017-11
• Subjects: Automatic karyotyping system -- Classification -- FPGA -- Human chromosome -- Kohonen -- Neural network -- System on chip; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.10.001
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: The Automatic karyotyping System is a computer-aided tool that automates the chromosome analysis and karyotyping processes, manually performed in most cytogenetic laboratories. Artificial neural networks (ANNs) have been widely used in chromosome classification due to their parallelism that reduces the computational complexity and time. However, existing classifiers are software-based, running on a computer that transforms the parallelism features of the ANNs into serial operations, thus significantly reducing their computing power . To efficiently address the above issue due to software implementation, we propose a Field-Programmable Gate Array-based System on Chip (SoC) architecture for human chromosome classification. The hardware implementation of such system can achieve the parallelism inherent to ANNs while reducing the power consumption and circuit size, thus the cost of designing such a system is reduced. The achieved part concerns the classification subsystem based on Kohonen neural network, which has been successfully tested on FPGA platform.