Appearance-based data augmentation for image datasets using contrast preserving sampling

Merchant, Alishan; Syed, Tahir Q.; Khan, Behraj; Munir, Rumaisah

doi:10.1109/icpr.2018.8545762

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…Contrast augmentation may create new images from old ones while preserving relative shapes and sizes of items in the images, which are commonly lost when using most traditional image augmentation techniques. The accuracy of deep learning models is increased by contrast enhancement, which highlights intensity variations in tissues and reveals patterns for differentiating between normal and fatty liver tissues [29]. Figure 6 shows an image after processing the contrast.…”

Section: Flippingmentioning

confidence: 99%

Efficient Detection of Hepatic Steatosis in Ultrasound Images Using Convolutional Neural Networks: A Comparative Study

Alshagathrh,

Musleh,

Alzubaidi

et al. 2023

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Introduction: Artificial Intelligence (AI) is widely used in medical studies to interpret imaging data and improve the efficiency of healthcare professionals. Nonalcoholic fatty liver disease (NAFLD) is a common liver abnormality associated with an increased risk of hepatic cirrhosis, hepatocellular carcinoma, and cardiovascular morbidity and mortality. This study explores the use of AI for automated detection of hepatic steatosis in ultrasound images. Background: Ultrasound is a non-invasive, cost-effective, and widely available method for hepatic steatosis screening. However, its accuracy depends on the operator's expertise, necessitating automated methods to enhance diagnostic accuracy. AI, particularly Convolutional Neural Network (CNN) models, can provide accurate and efficient analysis of ultrasound images, enabling automated detection, improving diagnostic accuracy, and facilitating real-time analysis. Problem Statement: This study aims to evaluate deep learning methods for binary classification of hepatic steatosis using ultrasound images. Methodology: Open-source data is used to prepare three groups (A, B, C) of ultrasound images in different sizes. Images are augmented using seven pre-processing approaches (resizing, flipping, rotating, zooming, contrasting, brightening, and wrapping) to increase image variations. Seven CNN classifiers (EfficientNet-B0, ResNet34, AlexNet, DenseNet121, ResNet18, ResNet50, and MobileNet_v2) are evaluated using stratified 10fold cross-validation. Six metrics (accuracy, sensitivity, specificity, precision, F1 score, and MCC) are employed, and the best-performing fold epochs are selected. Experiments and Results: The study evaluates seven models, finding EfficientNet-B0, ResNet34, DenseNet121, and AlexNet to perform well in groups A and B. EfficientNet-B0 shows the best overall performance. It achieves high scores for all six metrics, with accuracy rates of 98.9%, 98.4%, and 96.3% in groups A, B, and C, respectively. Discussion and Conclusion: EfficientNet-B0, ResNet34, and DenseNet121 exhibit potential for classifying fatty liver ultrasound images. EfficientNet-B0 demonstrates the best average accuracy, specificity, and sensitivity, although more training data is needed for generalization. Complete and mediumsized images are preferred for classification. Further evaluation of other classifiers is necessary to determine the best model.

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Section: Flippingmentioning

confidence: 99%