Parasitology Unveiled: Revolutionizing Microorganism Classification Through Deep Learning-Reference-Cited by-同舟云学术

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Parasitology Unveiled: Revolutionizing Microorganism Classification Through Deep Learning

Published:2024-05-02 Issue: Volume:3 Page:1163-1168
ISSN:
Container-title:2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT)
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Author:

Hosen Md Delwar¹,Bin Mohiuddin Arafath²,Sarker Nayamul³,Sakib Md Shadman³,Al Sakib Abdullah²,Dip Riyadot Hossain⁴,Ahmed Md. Redwan³,Haque Rezaul³

Affiliation:

1. North South University,Department of Electronics and Communication Engineering,Dhaka,Bangladesh

2. WestcliffUniversity,Department of Information Technology,California,USA

3. East West University,Department of Computer Science and Engineering,Dhaka,Bangladesh

4. Kyungdong University,Department of Computer Engineering,Gangwon-do,Republic of Korea

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10534252/10534311/10534322.pdf?arnumber=10534322

Reference18 articles.

1. Rapid detection of rice disease using microscopy image identification based on the synergistic judgment of texture and shape features and decision tree–confusion matrix method

2. Advantages and limitations of microscopy and molecular detections for diagnosis of soil-transmitted helminths: An overview

3. Deep Learning for Imaging and Detection of Microorganisms

4. Environmental microorganism classification using conditional random fields and deep convolutional neural networks

5. Environmental microorganism classification using optimized deep learning model

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