Publications by authors named "M Bagheri Harouni"

Breast density, or the amount of fibroglandular tissue (FGT) relative to the overall breast volume, increases the risk of developing breast cancer. Although previous studies have utilized deep learning to assess breast density, the limited public availability of data and quantitative tools hinders the development of better assessment tools. Our objective was to (1) create and share a large dataset of pixel-wise annotations according to well-defined criteria, and (2) develop, evaluate, and share an automated segmentation method for breast, FGT, and blood vessels using convolutional neural networks.

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Article Synopsis
  • Human lifestyle factors have worsened diseases like lung cancer, which is particularly deadly and can be detected early through Computer Aided Diagnosis (CAD) systems using CT scans.
  • Challenges in tumor detection include tumor location, irregular shapes, and poor quality of images, prompting researchers to explore deep learning algorithms for better diagnosis.
  • A new model using convolutional neural networks (CNN) has been proposed for segmenting tumors in CT scans, achieving impressive results with 98.33% accuracy, 99.25% validity, and 98.18% dice similarity, demonstrating its effectiveness in medical imaging.
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Introduction: Intergenerational conflict is one of the components which helps to inappropriate communication patterns and ineffective interactions. Intergenerational research aims to promote deeper understanding and respect between generations and helps to create more cohesive communities, suggesting that it can have numerous health and social benefits. Despite the importance of intergenerational relationships in the elderly, the older people's perceptions of intergenerational relationships in nursing homes are not well understood.

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The retina is the deepest layer of texture covering the rear of the eye, recorded by fundus images. Vessel detection and segmentation are useful in disease diagnosis. The retina's blood vessels could help diagnose maladies such as glaucoma, diabetic retinopathy, and blood pressure.

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Automatic identity verification is one of the most critical and research-demanding areas. One of the most effective and reliable identity verification methods is using unique human biological characteristics and biometrics. Among all types of biometrics, palm print is recognized as one of the most accurate and reliable identity verification methods.

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