International Journal of Advance Computational Engineering and Networking (IJACEN)
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Statistics report
Feb. 2021
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 94
Paper Published : 1276
No. of Authors : 3207
  Journal Paper

Paper Title
Classification of X-RAY images of Lungs for pneumonia using CNN

Abstract
Pneumonia is one of the most common respiratory disorders, it is sometimes very dangerous and life threatening. Early detection of such disorders is very important for better and faster treatment. X-ray images are one of the major methods of detecting pneumonia, therefore, an efficient image detection and classification technique is required to accurately classify X-ray images. We suggest a convolutional neural networks-based method to efficiently classify X-ray. The suggested method is implemented by the use of Keras, a Tens or Flow based Python library. ReLu and sigmoid are used as activation functions and Adam is used as the optimizer function. The data was trained with an accuracy of about 94% and testing accuracy of 92%. Keywords - Pneumonia, Neural Networks, Deep Learning, Keras, Classification, Image Augmentation.


Author - Nishanth B Jain, Nisarg S Devdhar, Likhitha V, Vaishnavi M P

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| Published on 2020-11-09
   
   
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