(Retracted) Deep belief network-based image processing for local directional segmentation in brain tumor detection

Doshi, Ruchi; Hiran, Kamal Kant; Doppala, Bhanu Prakash; Vyas, Ajay Kumar

doi:10.1117/1.jei.32.6.062502

Cited by 11 publications

(1 citation statement)

References 40 publications

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“…One area of study is defect detection, as explored by [22]. In the medical field, image processing has been utilized for a variety of purposes, such as in [43,6,31,9]. Climate detection and management is another area where image processing has been applied such as [24].…”

Section: Related Workmentioning

confidence: 99%

CAugment: An Approach to Diversifying Dataset by Combining Image Processing Operations

Gao

2023

ITC

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In deep learning, model quality is extremely important. Consequently, the quality and the sufficiency of the datasets for training models have attracted considerable attention from both industry and academia. Automatic data augmentation, which provides a means of using image processing operators to generate data from existing datasets, is quite effective in searching for mutants of the images and expanding the training datasets. However, existing automatic data augmentation techniques often fail to fully exploit the potential of the data, failing to balance the search efficiency and the model accuracy. This paper presents CAugment, a novel approach to diversifying image datasets by combining image processing operators. Given a training image dataset, CAugment is composed of: 1) the three-level evolutionary algorithm (TLEA) that employs three levels of atomic operations for augmenting the dataset and an adaptive strategy for decreasing granularity and 2) a design that uses the three-dimensional evaluation method (TDEM) and a dHash algorithm to measure the diversity of the dataset. The search space can be expanded, which further improves model accuracy during training. We use CAugment to augment the CIFAR-10/100 and SVHN datasets and use the augmented datasets to train the WideResNet and Shake-Shake models. Our results show that the amount of data increases linearly along with the training epochs; in addition, the models trained by the CAugment-augmented datasets outperform those trained by the datasets augmented by the other techniques by up to 17.9% in accuracy on the SVHN dataset.

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