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Construction of Deep Convolutional Neural Networks For Medical Image Classification

Construction of Deep Convolutional Neural Networks For Medical Image Classification

Rama A, Kumaravel A, Nalini C
Copyright: © 2019 |Volume: 9 |Issue: 2 |Pages: 15
ISSN: 2155-6997|EISSN: 2155-6989|EISBN13: 9781522567196|DOI: 10.4018/IJCVIP.2019040101
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MLA

Rama A, et al. "Construction of Deep Convolutional Neural Networks For Medical Image Classification." IJCVIP vol.9, no.2 2019: pp.1-15. http://doi.org/10.4018/IJCVIP.2019040101

APA

Rama A, Kumaravel A, & Nalini C. (2019). Construction of Deep Convolutional Neural Networks For Medical Image Classification. International Journal of Computer Vision and Image Processing (IJCVIP), 9(2), 1-15. http://doi.org/10.4018/IJCVIP.2019040101

Chicago

Rama A, Kumaravel A, and Nalini C. "Construction of Deep Convolutional Neural Networks For Medical Image Classification," International Journal of Computer Vision and Image Processing (IJCVIP) 9, no.2: 1-15. http://doi.org/10.4018/IJCVIP.2019040101

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Abstract

Implementing image processing tools demands its components produce better results in critical applications like medical image classification. TensorFlow is one open source with a machine learning framework for high performance and operates in heterogeneous environments. It heralds broad attention at a fine tuning of parameters for obtaining the final models, to obtain better performance. The main aim of this article is to prove the appropriate steps for the classification techniques for diagnosing the diseases with better accuracy. The proposed convolutional network is comprised of three convolutional layers, preceded by average pooling with a size equal to the size of the final feature maps. The final layer in this network has two outputs, corresponding to the number of classes considered to be either normal or abnormal. To train and evaluate such networks like the Deep Convolutional Neural Network (DCNN), a dataset of 2000 x-ray images of lungs was used and a comparative analysis between the proposed DCNN against previous methods is also made.

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