Skip to main content

Research Repository

Advanced Search

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

Ghani, A; Hodeify, R.; See, Chan Hwang; Keates, S.; Lee, D.J.; Bouridane, A.


A Ghani

R. Hodeify

S. Keates

D.J. Lee

A. Bouridane


In medical and health sciences, 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 plat-form. 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 was 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.

Journal Article Type Article
Acceptance Date Dec 15, 2022
Online Publication Date Dec 19, 2022
Publication Date 2022
Deposit Date Dec 15, 2022
Publicly Available Date Dec 19, 2022
Journal Electronics
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 11
Issue 24
Article Number 4234
Keywords artificial neural networks; cell classification; FPGAs; hardware accelerators; human kidney-damaged cells
Public URL


You might also like

Downloadable Citations