Combining DC-GAN with ResNet for blood cell image classification

Ma, Li; Shuai, Renjun; Ran, Xuming; Liu, Wenjia; Ye, Chao

doi:10.1007/s11517-020-02163-3

Cited by 93 publications

(36 citation statements)

References 42 publications

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“…Li Ma et al proposed a combination of DCGAN [ 17 ], and ResNet for blood cell image classification [ 18 ]. The authors introduced a new loss function that has improved the discriminative architecture of the GAN model.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of Microscopic Cell Images Obtained from Bone Marrow Aspirate Smears through Generative Adversarial Networks

Hazra

Byun

Kim³

et al. 2022

Biology

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Every year approximately 1.24 million people are diagnosed with blood cancer. While the rate increases each year, the availability of data for each kind of blood cancer remains scarce. It is essential to produce enough data for each blood cell type obtained from bone marrow aspirate smears to diagnose rare types of cancer. Generating data would help easy and quick diagnosis, which are the most critical factors in cancer. Generative adversarial networks (GAN) are the latest emerging framework for generating synthetic images and time-series data. This paper takes microscopic cell images, preprocesses them, and uses a hybrid GAN architecture to generate synthetic images of the cell types containing fewer data. We prepared a single dataset with expert intervention by combining images from three different sources. The final dataset consists of 12 cell types and has 33,177 microscopic cell images. We use the discriminator architecture of auxiliary classifier GAN (AC-GAN) and combine it with the Wasserstein GAN with gradient penalty model (WGAN-GP). We name our model as WGAN-GP-AC. The discriminator in our proposed model works to identify real and generated images and classify every image with a cell type. We provide experimental results demonstrating that our proposed model performs better than existing individual and hybrid GAN models in generating microscopic cell images. We use the generated synthetic data with classification models, and the results prove that the classification rate increases significantly. Classification models achieved 0.95 precision and 0.96 recall value for synthetic data, which is higher than the original, augmented, or combined datasets.

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Section: Introductionmentioning

confidence: 99%

Synthesis of Microscopic Cell Images Obtained from Bone Marrow Aspirate Smears through Generative Adversarial Networks

Hazra

Byun

Kim³

et al. 2022

Biology

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show abstract

“…At present, WGAN has been successfully applied in the classification of imbalanced biomedical images. For example, Ma et al [25] used a deep convolutional generative adversarial network (DC-GAN) for the data augmentation of white blood cells. Additionally, classification accuracy was improved by DC-GAN.…”

Section: Wgan-gpmentioning

confidence: 99%

An Imbalanced Image Classification Method for the Cell Cycle Phase

2021

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The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

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“…Finally, 182 CT images of liver lesions are used for verification, and the sensitivity and specificity of the network are improved after adding synthetic data enhancement. Ma et al [ 15 ] proposed a blood cell image classification framework based on Deep Convolutional Generative Adversarial Network (DC-GAN) and Residual Network (ResNet).…”

Section: Related Workmentioning

confidence: 99%

Multiclassification of Endoscopic Colonoscopy Images Based on Deep Transfer Learning

Wang

Feng

Song

et al. 2021

Computational and Mathematical Methods in Medicine

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With the continuous improvement of human living standards, dietary habits are constantly changing, which brings various bowel problems. Among them, the morbidity and mortality rates of colorectal cancer have maintained a significant upward trend. In recent years, the application of deep learning in the medical field has become increasingly spread aboard and deep. In a colonoscopy, Artificial Intelligence based on deep learning is mainly used to assist in the detection of colorectal polyps and the classification of colorectal lesions. But when it comes to classification, it can lead to confusion between polyps and other diseases. In order to accurately diagnose various diseases in the intestines and improve the classification accuracy of polyps, this work proposes a multiclassification method for medical colonoscopy images based on deep learning, which mainly classifies the four conditions of polyps, inflammation, tumor, and normal. In view of the relatively small number of data sets, the network firstly trained by transfer learning on ImageNet was used as the pretraining model, and the prior knowledge learned from the source domain learning task was applied to the classification task about intestinal illnesses. Then, we fine-tune the model to make it more suitable for the task of intestinal classification by our data sets. Finally, the model is applied to the multiclassification of medical colonoscopy images. Experimental results show that the method in this work can significantly improve the recognition rate of polyps while ensuring the classification accuracy of other categories, so as to assist the doctor in the diagnosis of surgical resection.

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Combining DC-GAN with ResNet for blood cell image classification

Cited by 93 publications

References 42 publications

Synthesis of Microscopic Cell Images Obtained from Bone Marrow Aspirate Smears through Generative Adversarial Networks

Synthesis of Microscopic Cell Images Obtained from Bone Marrow Aspirate Smears through Generative Adversarial Networks

An Imbalanced Image Classification Method for the Cell Cycle Phase

Multiclassification of Endoscopic Colonoscopy Images Based on Deep Transfer Learning

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