FPGA implementation of deep learning architecture for kidney cancer detection from histopathological images

Shyam Lal, Amit Kumar Chanchal, Jyoti Kini, Gopal Krishna Upadhyay

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Kidney cancer is the most common type of cancer, and designing an automated system to accurately classify the cancer grade is of paramount importance for a better prognosis of the disease from histopathological kidney cancer images. Application of deep learning neural networks (DLNNs) for histopathological image classification is thriving and implementation of these networks on edge devices has been gaining the ground correspondingly due to high computational power and low latency requirements. This paper designs an automated system that classifies histopathological kidney cancer images. For experimentation, we have collected Kidney histopathological images of Non-cancerous, cancerous, and their respective grade of Renal Cell Carcinoma (RCC) from Kasturba Medical College (KMC), Mangalore, Karnataka, India. We have implemented and analyzed performances of deep learning architectures on a Field Programmable Gate Array (FPGA) board. Results yield that the Inception-V3 network provides better accuracy for kidney cancer detection as compared to other deep learning models on Kidney histopathological images. Further, the DenseNet-169 network provides better accuracy for kidney cancer grading as compared to other existing deep learning architecture on the FPGA board.

Original languageEnglish
Pages (from-to)60583-60601
Number of pages19
JournalMultimedia Tools and Applications
Volume83
Issue number21
DOIs
Publication statusPublished - 06-2024

All Science Journal Classification (ASJC) codes

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications

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