Further Advantages of Data Augmentation on Convolutional Neural Networks

Hernández-García, Álex; König, Peter

doi:10.1007/978-3-030-01418-6_10

Cited by 78 publications

(41 citation statements)

References 19 publications

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“…Image augmentations have also been observed to improve convergence, generalization ability, and robustness of samples and have more advantages compared to other regularization techniques [4,12]. The limited size of datasets is a particularly widespread case in the field of medical image analysis because of expensive and laborintensive procedures to collect [30].…”

Section: Image Data Augmentationmentioning

confidence: 99%

A voting-based ensemble deep learning method focusing on image augmentation and preprocessing variations for tuberculosis detection

Taşçı

Uluturk

Uğur

2021

Neural Comput & Applic

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Tuberculosis (TB) is known as a potentially dangerous and infectious disease that affects mostly lungs worldwide. The detection and treatment of TB at an early stage are critical for preventing the disease and decreasing the risk of mortality and transmission of it to others. Nowadays, as the most common medical imaging technique, chest radiography (CXR) is useful for determining thoracic diseases. Computer-aided detection (CADe) systems are also crucial mechanisms to provide more reliable, efficient, and systematic approaches with accelerating the decision-making process of clinicians. In this study, we propose voting and preprocessing variations-based ensemble CNN model for TB detection. We utilize 40 different variations in fine-tuned CNN models based on InceptionV3 and Xception by also using CLAHE (contrast-limited adaptive histogram equalization) preprocessing technique and 10 different image transformations for data augmentation types. After analyzing all these combination schemes, three or five best classifier models are selected as base learners for voting operations. We apply the Bayesian optimization-based weighted voting and the average of probabilities as a combination rule in soft voting methods on two TB CXR image datasets to get better results in various numbers of models. The computational results indicate that the proposed method achieves 97.500% and 97.699% accuracy rates on Montgomery and Shenzhen datasets, respectively. Furthermore, our method outperforms state-of-the-art results for the two TB detection datasets in terms of accuracy rate.

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Section: Image Data Augmentationmentioning

confidence: 99%

A voting-based ensemble deep learning method focusing on image augmentation and preprocessing variations for tuberculosis detection

Taşçı

Uluturk

Uğur

2021

Neural Comput & Applic

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“…ConvNets are robust to imbalanced data, so addressing this imbalance by over or under sampling (for example) was unnecessary. We investigated image augmentation using 10 degrees of random rotation, 20% zoom in and out, 10% height and width shifts, 10% shearing, and horizontal flipping, as augmentation sometimes produces more robust classifiers [39]. We saw no improvement with image augmentation possibly due to issues with local optima and thus proceeded with the original images.…”

Section: Descriptive Statisticsmentioning

confidence: 98%

Deep Vision for Breast Cancer Classification and Segmentation

Fulton

McLeod

Dolezel

et al. 2021

Cancers

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(1) Background: Female breast cancer diagnoses odds have increased from 11:1 in 1975 to 8:1 today. Mammography false positive rates (FPR) are associated with overdiagnoses and overtreatment, while false negative rates (FNR) increase morbidity and mortality. (2) Methods: Deep vision supervised learning classifies 299 × 299 pixel de-noised mammography images as negative or non-negative using models built on 55,890 pre-processed training images and applied to 15,364 unseen test images. A small image representation from the fitted training model is returned to evaluate the portion of the loss function gradient with respect to the image that maximizes the classification probability. This gradient is then re-mapped back to the original images, highlighting the areas of the original image that are most influential for classification (perhaps masses or boundary areas). (3) Results: initial classification results were 97% accurate, 99% specific, and 83% sensitive. Gradient techniques for unsupervised region of interest mapping identified areas most associated with the classification results clearly on positive mammograms and might be used to support clinician analysis. (4) Conclusions: deep vision techniques hold promise for addressing the overdiagnoses and treatment, underdiagnoses, and automated region of interest identification on mammography.

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“…This step prevents accuracy decay and overfitting. In [20] the authors demonstrate the importance of data augmentation as a regulazier in the CNN classification model.…”

Section: Data Preprocessingmentioning

confidence: 99%

A Convolutional Neural Network for Lentigo Diagnosis

Zorgui

Chaabene

Bouaziz

et al. 2020

Lecture Notes in Computer Science

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Using Reflectance Confocal Microscopy (RCM) for lentigo diagnosis is today considered essential. Indeed, RCM allows fast data acquisition with a high spatial resolution of the skin. In this paper, we use a deep convolutional neural network (CNN) to perform RCM image classification in order to detect lentigo. The proposed method relies on an InceptionV3 architecture combined with data augmentation and transfer learning. The method is validated on RCM data and shows very efficient detection performance with more than 98% of accuracy.

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Further Advantages of Data Augmentation on Convolutional Neural Networks

Cited by 78 publications

References 19 publications

A voting-based ensemble deep learning method focusing on image augmentation and preprocessing variations for tuberculosis detection

A voting-based ensemble deep learning method focusing on image augmentation and preprocessing variations for tuberculosis detection

Deep Vision for Breast Cancer Classification and Segmentation

A Convolutional Neural Network for Lentigo Diagnosis

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