Version 1
: Received: 14 March 2021 / Approved: 16 March 2021 / Online: 16 March 2021 (09:32:11 CET)
How to cite:
Ahmed, A.; Tharwat, G.; Bouallegue, B.; Khattab, M.; Al Moustafa, A.; Matter, S. Classification of Chest X-ray Images Using Machine Learning Techniques. Preprints2021, 2021030408. https://doi.org/10.20944/preprints202103.0408.v1
Ahmed, A.; Tharwat, G.; Bouallegue, B.; Khattab, M.; Al Moustafa, A.; Matter, S. Classification of Chest X-ray Images Using Machine Learning Techniques. Preprints 2021, 2021030408. https://doi.org/10.20944/preprints202103.0408.v1
Ahmed, A.; Tharwat, G.; Bouallegue, B.; Khattab, M.; Al Moustafa, A.; Matter, S. Classification of Chest X-ray Images Using Machine Learning Techniques. Preprints2021, 2021030408. https://doi.org/10.20944/preprints202103.0408.v1
APA Style
Ahmed, A., Tharwat, G., Bouallegue, B., Khattab, M., Al Moustafa, A., & Matter, S. (2021). Classification of Chest X-ray Images Using Machine Learning Techniques. Preprints. https://doi.org/10.20944/preprints202103.0408.v1
Chicago/Turabian Style
Ahmed, A., Ahmad Al Moustafa and Safaa Matter. 2021 "Classification of Chest X-ray Images Using Machine Learning Techniques" Preprints. https://doi.org/10.20944/preprints202103.0408.v1
Abstract
Machine Learning has completely transformed health care system, which transmits medical data through IOT sensors. So it is very important to encrypt them to protect patient data. encrypting medical images from a performance perspective consumes time; hence the use of an auto encoder is essential. An auto encoder is used in this work to compress the image as a vector prior to the encryption process. The digital image passes across description function and a decoder to get back the image in the proposed work; various experiments are carried out on hyper parameters to achieve the highest outcome of the classification. The findings demonstrate that the combination of Mean Square Logarithmic Error as the loss function, ADA grad as an optimizer, two layers for the encoder, and another reverse for the decoder, RELU as the activation function generates the best auto encoder results. The combination of Mean square error (lose function), RMS prop (optimizer), three layers for the encoder and another reverse for the decoder, and RELU (activation function) has the best classification result. All the experiments with different hyper parameter has run almost very close to each other even when changing the number of layers. The running time is between 9 and 16 second for each epoch.
Keywords
Auto encoder; IoT; Image encryption; Artificial Neural Network; Machine Learning
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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