A Study on Cyber Security for Password Generation

Vaishnnave, M. P.

doi:10.22214/ijraset.2020.31614

Cited by 1 publication

(1 citation statement)

References 3 publications

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“…Nevertheless, ResNet50's high representational capacity and ability to leverage pre-trained weights make it a strong candidate for hepatic steatosis classification, where subtle visual cues may play a crucial role in accurate diagnosis. Its deep architecture can effectively capture intricate features in ultrasound images and extract discriminative features related to hepatic steatosis [35].…”

Section: Resnet50mentioning

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: Resnet50mentioning

confidence: 99%