2021 International Conference on Emerging Smart Computing and Informatics (ESCI) 2021
DOI: 10.1109/esci50559.2021.9396854
|View full text |Cite
|
Sign up to set email alerts
|

Application of Deep Convulational Neural Network in Medical Image Classification

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
3
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
2
1
1

Relationship

0
4

Authors

Journals

citations
Cited by 4 publications
(4 citation statements)
references
References 33 publications
0
3
0
Order By: Relevance
“…All the layers of this network help in the deeper classification of an image. Therefore, the classification accuracy of this network is much more higher and precise than any other model [24][25].…”
Section: Deep Learning Approachesmentioning
confidence: 92%
See 1 more Smart Citation
“…All the layers of this network help in the deeper classification of an image. Therefore, the classification accuracy of this network is much more higher and precise than any other model [24][25].…”
Section: Deep Learning Approachesmentioning
confidence: 92%
“…Deep Learning [20][21] is also a machine learning technique. It uses the architecture of the neural network that's why it's also known as a deep neural network.…”
Section: Deep Learning Approachesmentioning
confidence: 99%
“…Cell image classification with CNNs presents unique challenges, particularly due to the high vari-ability in cell images and the often limited size of annotated datasets [26]. Cells can exhibit a wide range of appearances even within the same category, due to differences in staining, imaging conditions, and biological variability.…”
Section: B Non Temporal Approachmentioning
confidence: 99%
“…Nowadays, the medical image classification technology has made great progress. Yadav et al [1] made a summary of CNN used in medical image classification technology, and reached the conclusion that using deep CNN is more effective for medical image classification. Jeyaraj et al [2] used CNN to classify oral cancer, with an accuracy of 91% and reached the same conclusion.…”
Section: Introductionmentioning
confidence: 99%