Semi-Supervised Learning for limited medical data using Generative Adversarial Network and Transfer Learning
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...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
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Institute of Electrical and Electronics Engineers Inc.
2020
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097534590&doi=10.1109%2fICCI51257.2020.9247724&partnerID=40&md5=96dfedfc3c60cf2c4a670bc679a73c78 http://eprints.utp.edu.my/29860/ |
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| Institution: | Universiti Teknologi Petronas |
| Summary: | 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. |
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