Deep learning based conventional neural network architecture for medical image classification

Deep learning based conventional neural network architecture for medical image classification

Ramesh NeelapuGolagani Lavanya Devi Kurapati Srinivasa Rao 

Department of CS & SE, Andhra University College of Engineering(A), Vishakapatnam, Andhra Pradesh, India

Corresponding Author Email: 
rameshauvsp@gmail.com
Page: 
169-182
|
DOI: 
https://doi.org/10.3166/TS.35.169-182
| |
Published: 
31 August 2018
| Citation

ACCESS

Abstract: 

The enactment of automatic medial image taxonomy using customary methods of machine learning and data mining mostly depend upon option of significant descriptive characteristics obtained from the medical images. Reorganization of those skins obliges domain-specific skillful awareness moreover not a forthright process. Here in this paper we are going to propose a deep learning based cnn’s named as deep cnn architecture. Which is a generic architecture and it accepts input as medical image data and produces the class or type of the decease. And we made comparison with the classical models like svm and elm.

Keywords: 

deep learning, neural networks, medical image classification, processing, CNN, SVM

1. Introduction
2. Related work
3. Proposed deep learning based convolutional neural network architecture
4. Experimental evaluation
5. Conclusion
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