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197 results on '"Sun, Yiran"'

31. Automated Classification and Segmentation and Feature Extraction from Breast Imaging Data.

Author

Sun, Yiran, Zhu, Zede, and Honarvar Shakibaei Asli, Barmak

Subjects
MACHINE learning, COMPUTER-aided diagnosis, MAGNETIC resonance imaging, CANCER diagnosis, FEATURE extraction, DEEP learning
Abstract

Breast cancer is the most common type of cancer in women and poses a significant health risk to women globally. Developments in computer-aided diagnosis (CAD) systems are focused on specific tasks of classification and segmentation, but few studies involve a completely integrated system. In this study, a comprehensive CAD system was proposed to screen ultrasound, mammograms and magnetic resonance imaging (MRI) of breast cancer, including image preprocessing, breast cancer classification, and tumour segmentation. First, the total variation filter was used for image denoising. Second, an optimised XGBoost machine learning model using EfficicnetB0 as feature extraction was proposed to classify breast images into normal and tumour. Third, after classifying the tumour images, a hybrid CNN deep learning model integrating the strengths of MobileNet and InceptionV3 was proposed to categorise tumour images into benign and malignant. Finally, Attention U-Net was used to segment tumours in annotated datasets while classical image segmentation methods were used for the others. The proposed models in the designed CAD system achieved an accuracy of 96.14% on the abnormal classification and 94.81% on tumour classification on the BUSI dataset, improving the effectiveness of automatic breast cancer diagnosis. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Early Breast Cancer Detection Using Artificial Intelligence Techniques Based on Advanced Image Processing Tools.

Author

Zhu, Zede, Sun, Yiran, and Honarvar Shakibaei Asli, Barmak

Subjects
TRANSFORMER models, IMAGE processing, IMAGE recognition (Computer vision), IMAGE analysis, EARLY detection of cancer, MAGNETIC resonance mammography
Abstract

The early detection of breast cancer is essential for improving treatment outcomes, and recent advancements in artificial intelligence (AI), combined with image processing techniques, have shown great potential in enhancing diagnostic accuracy. This study explores the effects of various image processing methods and AI models on the performance of early breast cancer diagnostic systems. By focusing on techniques such as Wiener filtering and total variation filtering, we aim to improve image quality and diagnostic precision. The novelty of this study lies in the comprehensive evaluation of these techniques across multiple medical imaging datasets, including a DCE-MRI dataset for breast-tumor image segmentation and classification (BreastDM) and the Breast Ultrasound Image (BUSI), Mammographic Image Analysis Society (MIAS), Breast Cancer Histopathological Image (BreakHis), and Digital Database for Screening Mammography (DDSM) datasets. The integration of advanced AI models, such as the vision transformer (ViT) and the U-KAN model—a U-Net structure combined with Kolmogorov–Arnold Networks (KANs)—is another key aspect, offering new insights into the efficacy of these approaches in different imaging contexts. Experiments revealed that Wiener filtering significantly improved image quality, achieving a peak signal-to-noise ratio (PSNR) of 23.06 dB and a structural similarity index measure (SSIM) of 0.79 using the BreastDM dataset and a PSNR of 20.09 dB with an SSIM of 0.35 using the BUSI dataset. When combined filtering techniques were applied, the results varied, with the MIAS dataset showing a decrease in SSIM and an increase in the mean squared error (MSE), while the BUSI dataset exhibited enhanced perceptual quality and structural preservation. The vision transformer (ViT) framework excelled in processing complex image data, particularly with the BreastDM and BUSI datasets. Notably, the Wiener filter using the BreastDM dataset resulted in an accuracy of 96.9% and a recall of 96.7%, while the combined filtering approach further enhanced these metrics to 99.3% accuracy and 98.3% recall. In the BUSI dataset, the Wiener filter achieved an accuracy of 98.0% and a specificity of 98.5%. Additionally, the U-KAN model demonstrated superior performance in breast cancer lesion segmentation, outperforming traditional models like U-Net and U-Net++ across datasets, with an accuracy of 93.3% and a sensitivity of 97.4% in the BUSI dataset. These findings highlight the importance of dataset-specific preprocessing techniques and the potential of advanced AI models like ViT and U-KAN to significantly improve the accuracy of early breast cancer diagnostics. [ABSTRACT FROM AUTHOR]

Published
2024
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43. Enhancing the Management of Non-Specific Neck Pain through Gamification: Design and Efficacy of a Health Application.

Author

Sun, Yiran, Xian, Yanjie, Lin, Hongbo, and Sun, Xing

Subjects
*NECK pain, *CERVICAL vertebrae, *PAIN management, *CHRONIC pain, *DIGITAL health
Abstract

Chronic non-specific neck pain (CNNP) poses a substantial health and economic burden in China. This study introduces a gamified motion-sensing health application framework to address the limitations of existing health applications. The gamified cervical spine somatic exercise application employs motion capture technology alongside the smartphone's built-in sensors to simulate accurate somatic interactions. Controlled experiments and data analyses demonstrated that the application significantly outperformed traditional text and video interventions in relieving participants' neck pain by increasing their average daily activity and compliance with the cervical spine exercise routine. The neck pain level of the participants is quantified by the Neck Disability Index (NDI). The results from the controlled experiments demonstrate that this gamified approach significantly decreases the Neck Disability Index (NDI) score from 1.54 to 1.24, highlighting its ability to alleviate neck pain and increase user compliance. [ABSTRACT FROM AUTHOR]

Published
2024
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