High-level Features in Deeper Deep Learning Layers for Breast Cancer Classification

Razali, Noor Fadzilah; Isa, Iza Sazanita; Sulaiman, Siti Noraini; Karim, Noor Khairiah A.; Osman, Muhammad Khusairi

doi:10.1109/iccsce52189.2021.9530911

Cited by 4 publications

(2 citation statements)

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“…The same method gets to an 88% accuracy rate when only basic data augmentation techniques are adopted. Razali et al [ 84 ] reached an excellent 99% accuracy rate on InBreast and DDSM with basic augmentation techniques on two datasets. However, it would be worth investigating any further improvement with advanced data augmentation techniques.…”

Section: Discussionmentioning

confidence: 99%

“…Fundamental transformations such as flipping, rotation, shifting and zooming proved effective in Shi et al’s method [ 28 ], with a gain of 32.3% on the validation accuracy rate over MIAS. Razali et al [ 84 ] obtained post-augmentation performances in breast cancer classification 26% higher. Only rotation, flipping and shearing were applied to INBreast and CBIS-DDSM.…”

Section: Discussionmentioning

confidence: 99%

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Image Augmentation Techniques for Mammogram Analysis

et al. 2022

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Research in the medical imaging field using deep learning approaches has become progressively contingent. Scientific findings reveal that supervised deep learning methods’ performance heavily depends on training set size, which expert radiologists must manually annotate. The latter is quite a tiring and time-consuming task. Therefore, most of the freely accessible biomedical image datasets are small-sized. Furthermore, it is challenging to have big-sized medical image datasets due to privacy and legal issues. Consequently, not a small number of supervised deep learning models are prone to overfitting and cannot produce generalized output. One of the most popular methods to mitigate the issue above goes under the name of data augmentation. This technique helps increase training set size by utilizing various transformations and has been publicized to improve the model performance when tested on new data. This article surveyed different data augmentation techniques employed on mammogram images. The article aims to provide insights into basic and deep learning-based augmentation techniques.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Image Augmentation Techniques for Mammogram Analysis

et al. 2022

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Medical image data augmentation: techniques, comparisons and interpretations

Göçeri

2023

Artif Intell Rev

113

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Designing deep learning based methods with medical images has always been an attractive area of research to assist clinicians in rapid examination and accurate diagnosis. Those methods need a large number of datasets including all variations in their training stages. On the other hand, medical images are always scarce due to several reasons, such as not enough patients for some diseases, patients do not want to allow their images to be used, lack of medical equipment or equipment, inability to obtain images that meet the desired criteria. This issue leads to bias in datasets, overfitting, and inaccurate results. Data augmentation is a common solution to overcome this issue and various augmentation techniques have been applied to different types of images in the literature. However, it is not clear which data augmentation technique provides more efficient results for which image type since different diseases are handled, different network architectures are used, and these architectures are trained and tested with different numbers of data sets in the literature. Therefore, in this work, the augmentation techniques used to improve performances of deep learning based diagnosis of the diseases in different organs (brain, lung, breast, and eye) from different imaging modalities (MR, CT, mammography, and fundoscopy) have been examined. Also, the most commonly used augmentation methods have been implemented, and their effectiveness in classifications with a deep network has been discussed based on quantitative performance evaluations. Experiments indicated that augmentation techniques should be chosen carefully according to image types.

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Effects of Data Augmentation Techniques on Classification Performance in Knee MRIs

KÜÇÜK,

UĞur

2023

2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)

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High-level Features in Deeper Deep Learning Layers for Breast Cancer Classification

Cited by 4 publications

References 20 publications

Image Augmentation Techniques for Mammogram Analysis

Image Augmentation Techniques for Mammogram Analysis

Medical image data augmentation: techniques, comparisons and interpretations

Effects of Data Augmentation Techniques on Classification Performance in Knee MRIs

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