Semi-Supervised Learning for limited medical data using Generative Adversarial Network and Transfer Learning

Amin, I. and Hassan, S. and Jaafar, J. (2020) Semi-Supervised Learning for limited medical data using Generative Adversarial Network and Transfer Learning. In: UNSPECIFIED.

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Abstract

Deep Learning is progressively becoming popular for computer based automated diagnosis of diseases. Deep Learning algorithms necessitate a large amount of data for training which is hard to acquire for medical problems. Similarly, annotation of medical images can be done with the help of specialized doctors only. This paper presents a semi-supervised learning based model that combines the capabilities of generative adversarial network (GAN) and transfer learning. The proposed model does not demand a large amount of data and can be trained using a small number of images. To evaluate the performance of the model, it is trained and tested on publicly available chest Xray dataset. Better classification accuracy of 94.73 is achieved for normal X-ray images and the ones with pneumonia. © 2020 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Impact Factor: cited By 0
Uncontrolled Keywords: Data communication systems; Deep learning; Diagnosis; Intelligent computing; Medical imaging; Medical problems; Semi-supervised learning; Transfer learning, Adversarial networks; Automated diagnosis; Classification accuracy; Large amounts; Medical data; X-ray image, Learning algorithms
Depositing User: Ms Sharifah Fahimah Saiyed Yeop
Date Deposited: 25 Mar 2022 02:58
Last Modified: 25 Mar 2022 02:58
URI: http://scholars.utp.edu.my/id/eprint/29860

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