Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Classification of Chest X-ray Images Using Machine Learning Techniques

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. Preprints 2021, 2021030408 (doi: 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 (doi: 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.

Subject Areas

Auto encoder; IoT; Image encryption; Artificial Neural Network; Machine Learning

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.