Research Article

Deep Learning Approaches to Pattern Recognition in Uterine Fibroid Detection

Wai Yie Leong 1 *
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1 Faculty of Engineering and Quantity Surveying, INTI International University, Nilai 71800, Malaysia* Corresponding Author
International Journal of Clinical Medicine and Bioengineering, 4(4), 2024, 1-9, https://doi.org/10.35745/ijcmb2024v04.04.0001
Submitted: 12 September 2024, Published: 30 December 2024
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ABSTRACT

Uterine fibroids are non-cancerous growths in the uterus that can cause a variety of symptoms, including heavy menstrual bleeding, pain, and reproductive issues. Accurate detection and diagnosis are crucial for effective treatment. Recent advancements in deep learning, specifically convolutional neural networks (CNNs), have shown significant promise in improving the accuracy and speed of fibroid detection using medical imaging modalities such as ultrasound and MRI. This paper explores state-of-the-art deep learning approaches to pattern recognition for uterine fibroid detection, comparing different techniques, datasets, and model architectures. Experimental results demonstrate the effectiveness of CNN-based methods, leading to higher accuracy, reduced false-positive rates, and improved clinical outcomes.
Keywords: Uterine Fibroids, Pattern Recognition, Deep Learning, Convolutional Neural Networks, Ultrasound Imaging, MRI, Detection, Medical Image Analysis

CITATION (APA)

Leong, W. Y. (2024). Deep Learning Approaches to Pattern Recognition in Uterine Fibroid Detection. International Journal of Clinical Medicine and Bioengineering, 4(4), 1-9. https://doi.org/10.35745/ijcmb2024v04.04.0001

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