Parasitized Malarial Infected Cell Detection through Variance Scaling Kernel Initializer Conv2D-Reference-Cited by-同舟云学术

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Parasitized Malarial Infected Cell Detection through Variance Scaling Kernel Initializer Conv2D

Published:2024-01-11 Issue: Volume:10 Page:1-6
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Container-title:2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)
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Author:

Devi M. Shyamala¹,Umanandhini D.¹,Joshi Aparna²,Dontamsetti Gowtham³,S Shreyas³

Affiliation:

1. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology,Computer Science & Engineering,Chennai,Tamilnadu,India

2. Pimpri Chinchwad College of Engineering,Computer Science & Engineering,Pune,India

3. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology,Artificial Intelligence and Data Science,Chennai,Tamilnadu,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10468568/10468675/10469326.pdf?arnumber=10469326

Reference26 articles.

1. Automated Detection of Malaria implemented by Deep Learning in Pytorch

2. Computational approach for diagnosis of malaria through classification of malaria parasite from microscopic image of blood smear

3. Automated Complete Blood Cell Count and Malaria Pathogen Detection Using Convolution Neural Network

4. Malaria Detection using Deep Learning

5. Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images

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