Computer Vision-Based Kidney’s (HK-2) Damaged Cells Classification with Reconfigurable Hardware Accelerator (FPGA)

Ghani, A., Hodeify, R., See, C. H., Keates, S., Lee, D.-J. and Bouridane, A. (2022) Computer Vision-Based Kidney’s (HK-2) Damaged Cells Classification with Reconfigurable Hardware Accelerator (FPGA). Electronics, 11 (24). pp. 1-17. ISSN 2079-9292

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Abstract

In medical and health sciences, the detection of cell injury plays an important role in diagnosis, personal treatment and disease prevention. Despite recent advancements in tools and methods for image classification, it is challenging to classify cell images with higher precision and accuracy. Cell classification based on computer vision offers significant benefits in biomedicine and healthcare. There have been studies reported where cell classification techniques have been complemented by Artificial Intelligence-based classifiers such as Convolutional Neural Networks. These classifiers suffer from the drawback of the scale of computational resources required for training and hence do not offer real-time classification capabilities for an embedded system platform. Field Programmable Gate Arrays (FPGAs) offer the flexibility of hardware reconfiguration and have emerged as a viable platform for algorithm acceleration. Given that the logic resources and on-chip memory available on a single device are still limited, hardware/software co-design is proposed where image pre-processing and network training were performed in software, and trained architectures were mapped onto an FPGA device (Nexys4DDR) for real-time cell classification. This paper demonstrates that the embedded hardware-based cell classifier performs with almost 100% accuracy in detecting different types of damaged kidney cells.

Publication Type: Articles
Uncontrolled Keywords: Artificial neural networks, cell classification, FPGAs, hardware accelerators, human kidney-damaged cells
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Q Science > QR Microbiology
R Medicine > R Medicine (General)
R Medicine > RC Internal medicine
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Academic Areas > Department of Engineering, Computing and Design > Electrical Engineering
Academic Areas > Vice Chancellor's Group
Research Entities > Centre for Future Technologies
Related URLs:
SWORD Depositor: Publications Router Jisc
Depositing User: Publications Router Jisc
Date Deposited: 21 Feb 2023 11:37
Last Modified: 04 Jun 2024 12:46
URI: https://eprints.chi.ac.uk/id/eprint/6650

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