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ISSN Approved Journal No: 2320-2882 | Impact factor: 7.97 | ESTD Year: 2013
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Published Paper Details:
Paper Title
Blood Cells Count Using Deep Learning (ResNet-50)
Authors
Shete Tushar Vitthal, Shete Payal Santosh, Buchake Sarika Kisan, Dr. A. A. Khatri
Keywords
Blood cells, ResNet, Deep learning, Convolutional Neural Networks, blood cell count, machine learning, image classification, image processing, blood cells detection.
Abstract
The Complete Blood cells count has an important role in medical diagnosis to check common fitness conditions. Blood cells are traditionally counted using Hemocytometer with laboratory compounds and chemical compounds. It is so time consuming. In this paper, we propose a deep learning neural network based architecture to accurately detect and count various types of blood cells (RBCs, WBCs, Platelets, etc.). The Convolutional Neural Network is used for classification and ResNet is used for feature extraction and improving the accuracy of blood cells counting. ResNet algorithm gives about 99% accuracy which is very good
IJCRT's Publication Details
Unique Identification Number - IJCRT2410644
Paper ID - 271420
Page Number(s) - f508-f512
Pubished in - Volume 12 | Issue 10 | October 2024
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Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Shete Tushar Vitthal, Shete Payal Santosh, Buchake Sarika Kisan, Dr. A. A. Khatri, "Blood Cells Count Using Deep Learning (ResNet-50)", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 10, pp.f508-f512, October 2024, Available at :http://www.ijcrt.org/papers/IJCRT2410644.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
ISSN and 7.97 Impact Factor Details
ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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