Image Augmentation Using Radial Transform for Training Deep Neural Networks

Salehinejad, Hojjat; Valaee, Shahrokh; Dowdell, Tim; Barfett, Joseph

doi:10.1109/icassp.2018.8462241

Cited by 60 publications

(35 citation statements)

References 11 publications

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“…We also plan to combine other geometric transformation techniques with GANs to augment EM images. For example, in [51], radial transformation is used in medical image augmentation for training deep neural networks. We will combine radial transformation with GANs to augment EM images.…”

Section: Discussionmentioning

confidence: 99%

An Enhanced Framework of Generative Adversarial Networks (EF-GANs) for Environmental Microorganism Image Augmentation With Limited Rotation-Invariant Training Data

Rahaman

et al. 2020

IEEE Access

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The main obstacle to image augmentation with Generative Adversarial Networks (GANs) is the need for a large amount of training data, but this is difficult for small datasets like Environmental Microorganisms (EMs). EM image analysis plays a vital role in environmental monitoring and protection, but it is often encountered with small datasets due to the difficulty of EM image collection. To this end, we propose an Enhanced Framework of GANs (EF-GANs) that combines geometric transformation methods and GANs for EM image augmentation. First of all, the color of an EM image has an insignificant impact on its class label, based on this fact, we perform color space augmentation to the original EM images. Secondly, we train EF-GANs with augmented EM images to generate utterly new EM images. Finally, we rotate the generated samples in various directions to obtain a more natural performance. In this study, we use VGG16 and ResNet50 networks to evaluate the proposed EF-GANs on 21 different types of EMs (420 EM images). It is observed that the average precision (AP) of VGG16 increases between 4.5% and 84.1% in 20 EM classes and one class remains unchanged. The AP of Resnet50 rises between 8.7% and 38.7% in 12 EM classes and reaches 100% in two EM classes. Furthermore, to reflect the generalization performance of EF-GANs, we employ an utterly new EM image dataset (630 EM images) to test the previous VGG16 networks. We select the VGG16 networks with original and optimal settings for all the EM classes, and for testing, optimal settings for a single EM class is considered. In the 20 of 21 one-vs-rest EM image classification tasks, the AP of VGG16 increases between 1.66% and 88.1%. The results demonstrate that the proposed EF-GANs can achieve outstanding performance in augmenting single EM images with high quality and resolution, thus, to improve the APs of EM image classification.

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Section: Discussionmentioning

confidence: 99%

An Enhanced Framework of Generative Adversarial Networks (EF-GANs) for Environmental Microorganism Image Augmentation With Limited Rotation-Invariant Training Data

Rahaman

et al. 2020

IEEE Access

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“…In our study, two training datasets were organized: the pre-training dataset and the main training dataset. Pre-training dataset: To increase data diversity and enhance the relationship between adjacent tissues in the training process, for each raw image, we applied radial transform (RT) [ 20 ] to generate 6 augmented images to build a pre-training dataset. In total, this dataset includes 1362 positive samples and 2160 negative ones.…”

Section: Methodsmentioning

confidence: 99%

A Deep Learning Method for Alerting Emergency Physicians about the Presence of Subphrenic Free Air on Chest Radiographs

Tsai

Tseng

et al. 2021

JCM

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Hollow organ perforation can precipitate a life-threatening emergency due to peritonitis followed by fulminant sepsis and fatal circulatory collapse. Pneumoperitoneum is typically detected as subphrenic free air on frontal chest X-ray images; however, treatment is reliant on accurate interpretation of radiographs in a timely manner. Unfortunately, it is not uncommon to have misdiagnoses made by emergency physicians who have insufficient experience or who are too busy and overloaded by multitasking. It is essential to develop an automated method for reviewing frontal chest X-ray images to alert emergency physicians in a timely manner about the life-threatening condition of hollow organ perforation that mandates an immediate second look. In this study, a deep learning-based approach making use of convolutional neural networks for the detection of subphrenic free air is proposed. A total of 667 chest X-ray images were collected at a local hospital, where 587 images (positive/negative: 267/400) were used for training and 80 images (40/40) for testing. This method achieved 0.875, 0.825, and 0.889 in sensitivity, specificity, and AUC score, respectively. It may provide a sensitive adjunctive screening tool to detect pneumoperitoneum on images read by emergency physicians who have insufficient clinical experience or who are too busy and overloaded by multitasking.

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“…After data initialization, we added new images by rotating the images 90 • , 180 • , and 270 • , mirroring each image along two diagonals, horizontal and vertical mirroring of each image, changing image brightness, contrast, saturation or hue, adding Gaussian noise, salt and pepper noise to images, or transforming polar coordinates. The adjustments made to the transforming polar coordinates are reflected in (1) and (2) [13]. The polar coordinate transformation is used to deform an image and obtain its different defect shapes.…”

Section: ) Datasetmentioning

confidence: 99%

A Fully Convolutional Neural Network for Wood Defect Location and Identification

Liu

et al. 2019

IEEE Access

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Defect detection on solid wood surface has two main problems: (1) the real-time performance of the available methods are poor despite good detection accuracy, and (2) the defect extraction process is complicated. Here, we propose a mixed, fully convolutional neural network (Mix-FCN) to detect the location of wood defects and classify the types of defects from the wood surface images automatically. The images were collected first by a data acquisition device developed in our laboratory. We then employed TensorFlow and Python language to construct a VGG16 model. We used two kinds of datasets (dataset1 and dataset2) to maximize the limited, collected data and enable the Mix-FCN to converge rapidly during training. The weights of the filters in front of the Mix-FCN during training were initialized from the trained VGG16 model. The weights of the VGG16 net were learned by dataset1. Our model was trained, validated, and tested by dataset 2. Overall classification accuracy (OCA), pixel accuracy (PA), mean intersection over union, detection rate, missing alarm, false alarm rate, and precision were used to evaluate the network, and the performance was good based on the seven evaluation indicators. We achieved 99.14% OCA and 91.31% PA, and a batch of 50 images required only 0.368 s of detection time. Our proposed method has better accuracy and less detection time compared to the previous methods of wood detection. INDEX TERMS Deep learning, full convolutional neural network, transfer learning, wood defects detection.

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Image Augmentation Using Radial Transform for Training Deep Neural Networks

Cited by 60 publications

References 11 publications

An Enhanced Framework of Generative Adversarial Networks (EF-GANs) for Environmental Microorganism Image Augmentation With Limited Rotation-Invariant Training Data

An Enhanced Framework of Generative Adversarial Networks (EF-GANs) for Environmental Microorganism Image Augmentation With Limited Rotation-Invariant Training Data

A Deep Learning Method for Alerting Emergency Physicians about the Presence of Subphrenic Free Air on Chest Radiographs

A Fully Convolutional Neural Network for Wood Defect Location and Identification

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