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

Amin, Ibrar; Hassan, Saima; Jaafar, Jafreezal

doi:10.1109/icci51257.2020.9247724

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…GAN-based semi-supervised medical image classification . To overcome the common data shortage in the field of medical image classification, several works [ 4 , 9 , 10 , 24 , 25 ] have been proposed to use GAN for DA and classify the images in a semi-supervised way. The existing works can be divided into two approaches.…”

Section: Related Workmentioning

confidence: 99%

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GSDA: Generative adversarial network-based semi-supervised data augmentation for ultrasound image classification

Liu,

Lv,

Lee

et al. 2023

Heliyon

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Section: Related Workmentioning

confidence: 99%

“…The existing works can be divided into two approaches. The first is to train GAN solely and use the

of GAN as a classifier [ 9 , 24 ]. The second is to train GAN first and then use separate CNN as a separate classifier [ 4 , 10 , 25 ].…”

Section: Related Workmentioning

confidence: 99%

GSDA: Generative adversarial network-based semi-supervised data augmentation for ultrasound image classification

Liu,

Lv,

Lee

et al. 2023

Heliyon

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“…Semi-supervised learning (SSL) has also been used to address Pneumonia detection problem using X-rays. For example, Amin et al [24] proposed an SSL-based model utilizing Generative Adversarial Networks (GANs) [25]. GANs are used, along with transfer learning, utilizing a pretrained VGG16 model [26] as the discriminator.…”

Section: X-ray Classification Using Deep Learningmentioning

confidence: 99%

Investigating the Performance of FixMatch for COVID-19 Detection in Chest X-rays

2022

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The advent of the COVID-19 pandemic has resulted in medical resources being stretched to their limits. Chest X-rays are one method of diagnosing COVID-19; they are used due to their high efficacy. However, detecting COVID-19 manually by using these images is time-consuming and expensive. While neural networks can be trained to detect COVID-19, doing so requires large amounts of labeled data, which are expensive to collect and code. One approach is to use semi-supervised neural networks to detect COVID-19 based on a very small number of labeled images. This paper explores how well such an approach could work. The FixMatch algorithm, which is a state-of-the-art semi-supervised classification algorithm, was trained on chest X-rays to detect COVID-19, Viral Pneumonia, Bacterial Pneumonia and Lung Opacity. The model was trained with decreasing levels of labeled data and compared with the best supervised CNN models, using transfer learning. FixMatch was able to achieve a COVID F1-score of 0.94 with only 80 labeled samples per class and an overall macro-average F1-score of 0.68 with only 20 labeled samples per class. Furthermore, an exploratory analysis was conducted to determine the performance of FixMatch to detect COVID-19 when trained with imbalanced data. The results show a predictable drop in performance as compared to training with uniform data; however, a statistical analysis suggests that FixMatch may be somewhat robust to data imbalance, as in many cases, and the same types of mistakes are made when the amount of labeled data is decreased.

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“…Furthermore, it has been demonstrated that with less labelled data, the semi-supervised GAN (SGAN) can achieve performance comparable to a traditional supervised Convolutional Neural Net-work (CNN) for medical images [17]. Motivated by the existing research, we propose a model in this paper (which is an extension of our conference paper [18] ) to detect malaria, which is based on transfer learning (TL) and SGAN (TL-SGAN). Unlike a conventional CNN model, the proposed TL-SGAN is trained with fewer data.…”

Section: Introductionmentioning

confidence: 99%

“…This research is an extension of our research work published in [18]. Rest of the paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification

Amin¹,

Hassan²,

Belhaouari³

et al. 2023

Computers, Materials &Amp; Continua

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Malaria is a lethal disease responsible for thousands of deaths worldwide every year. Manual methods of malaria diagnosis are timeconsuming that require a great deal of human expertise and efforts. Computerbased automated diagnosis of diseases is progressively becoming popular. Although deep learning models show high performance in the medical field, it demands a large volume of data for training which is hard to acquire for medical problems. Similarly, labeling of medical images can be done with the help of medical experts only. Several recent studies have utilized deep learning models to develop efficient malaria diagnostic system, which showed promising results. However, the most common problem with these models is that they need a large amount of data for training. This paper presents a computer-aided malaria diagnosis system that combines a semi-supervised generative adversarial network and transfer learning. The proposed model is trained in a semi-supervised manner and requires less training data than conventional deep learning models. Performance of the proposed model is evaluated on a publicly available dataset of blood smear images (with malariainfected and normal class) and achieved a classification accuracy of 96.6%.

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

Cited by 5 publications

References 17 publications

GSDA: Generative adversarial network-based semi-supervised data augmentation for ultrasound image classification

GSDA: Generative adversarial network-based semi-supervised data augmentation for ultrasound image classification

Investigating the Performance of FixMatch for COVID-19 Detection in Chest X-rays

Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification

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