TY  - CONF
PB  - Institute of Electrical and Electronics Engineers Inc.
N2  - 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.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097534590&doi=10.1109%2fICCI51257.2020.9247724&partnerID=40&md5=96dfedfc3c60cf2c4a670bc679a73c78
AV  - none
SN  - 9781728154473
TI  - Semi-Supervised Learning for limited medical data using Generative Adversarial Network and Transfer Learning
ID  - scholars12638
A1  - Amin, I.
A1  - Hassan, S.
A1  - Jaafar, J.
EP  - 10
Y1  - 2020///
KW  - Data communication systems; Deep learning; Diagnosis; Intelligent computing; Medical imaging; Medical problems; Semi-supervised learning; Transfer learning
KW  -  Adversarial networks; Automated diagnosis; Classification accuracy; Large amounts; Medical data; X-ray image
KW  -  Learning algorithms
N1  - cited By 4; Conference of 2020 International Conference on Computational Intelligence, ICCI 2020 ; Conference Date: 8 October 2020 Through 9 October 2020; Conference Code:164916
SP  - 5
ER  -