0% Complete
English
صفحه اصلی
/
چهاردهمین کنفرانس بین المللی فناوری اطلاعات و دانش
Improving Deep Neural Network Accelerator for Malaria Diseased Blood Cells using FPGA
نویسندگان :
Hadi Rezaeikarjani
1
Mojtaba Valinataj
2
1- دانشگاه صنعتی نوشیروانی بابل
2- دانشگاه صنعتی نوشیروانی بابل
کلمات کلیدی :
Hardware Accelerators،Malaria Disease،FPGA،Disease Detection with Neural Networks،Neural Networks،Medical Diagnosis
چکیده :
The escalating computational demands of deep neural networks across various applications have driven the adoption of hardware accelerators. These specialized hardware devices are tailor-made for specific computational tasks, offering enhanced efficiency compared to conventional computer systems. In medical diagnosis applications, particularly the detection of malaria-infected blood cells, hardware accelerators play a pivotal role. This paper explores the augmentation and acceleration of malaria-infected blood cell detection by leveraging FPGA-based hardware accelerators with deep neural networks. The significance of this research is twofold. Firstly, rapid and precise processing of medical images is imperative in diagnosing malaria. FPGA-based hardware accelerators excel in parallel processing and high efficiency, significantly expediting disease detection, a crucial advantage during outbreaks. Secondly, the intricate architectures and numerous parameters of deep neural networks demand efficient implementation. Hardware accelerators, notably FPGA-based ones, facilitate precise and efficient model execution, enhancing diagnosis accuracy, a paramount factor in disease detection. The study adopts an artificial neural network with a Multilayer Perceptron (MLP) architecture and implements various hardware units, resulting in substantially faster malaria-infected cell detection. The outcomes demonstrate an impressive accuracy increase from 94.76% to 98.27% and a significant reduction in latency from 5.93 nanoseconds to 0.397 nanoseconds in the hardware implementation. Moreover, the output representation has been improved, transitioning from a matrix display to a visually interpretable format with distinct colors, enabling real-time disease detection.
لیست مقالات
لیست مقالات بایگانی شده
Binary water stream algorithm: a new meta-heuristic optimization technique
Faezeh Rahimi Sebdani - Mehdi Nasri
استخراج موارد آزمون سطح برونمتد و درونکلاس از برنامههای شئگرا
محمد قرشی - حسن حقیقی
An Improved Drone Detection Method Using Deep Learning for Augmentation Detection Speed
Mohammad Bahrami - Seyyed Amir Asghari - Mohammadreza Binesh Marvasti - Sajjad Ansaria
Detection of Backdoor Attacks in Neural Networks Using Input Optimization
Parsa Hashemi Khorsand - Ahmad Nickabadi
ML-based Optical Fibre Fault Detection in Smart Surveillance and Traffic Systems
Rushil Patel - Sana Narmawala - Nikunjkumar Mahida - Rajesh Gupta - Sudeep Tanwar - Hossein Shahinzadeh
Embedded speech encoder for low-resource languages
Alireza A.Tabatabaei - Pouria Sameti - Ali Bohlooli
Sentiment Analysis of the Amazon Customers Using the BiGRU Neural Network Enhanced by Attention Mechanism
Sara Sinan Salman al-Abedi - Keyvan Mohebbi
An Eco-Friendly Cosmopolitan (EFC) by Recycling Scientific/Industrial Towns (RSITs)
Engineer Reza Khalilian - Dr. Abdalhossein Rezai - Dr. Mohammadreza Talakesh
Silicon photonic microring resonators: A Novel optical router based on Negative-First routing algorithm
Negin Bagheri Renani - Elham Yaghoubi
A U-Net architecture with graph attention networks to accurately define tooth boundaries
Ehsan Akefi - Hassan Khotanlou
بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 42.5.2