Deep learning based uterine fibroid detection in ultrasound images.

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING BMC Medical Imaging Pub Date : 2024-08-19 DOI:10.1186/s12880-024-01389-z
Haibin Xi, Wenjing Wang
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引用次数: 0

Abstract

Uterine fibroids are common benign tumors originating from the uterus's smooth muscle layer, often leading to symptoms such as pelvic pain, and reproductive issues. Early detection is crucial to prevent complications such as infertility or the need for invasive treatments like hysterectomy. One of the main challenges in diagnosing uterine fibroids is the lack of specific symptoms, which can mimic other gynecological conditions. This often leads to under-diagnosis or misdiagnosis, delaying appropriate management. In this research, an attention based fine-tuned EfficientNetB0 model is proposed for the classification of uterine fibroids from ultrasound images. Attention mechanisms, permit the model to focus on particular parts of an image and move forward the model's execution by empowering it to specifically go to imperative highlights whereas overlooking irrelevant ones. The proposed approach has used a total of 1990 images divided into two classes: Non-uterine fibroid and uterine fibroid. The data augmentation methods have been connected to improve generalization and strength by exposing it to a wider range of varieties within the training data. The proposed model has obtained the value of accuracy as 0.99. Future research should focus on improving the accuracy and efficiency of diagnostic techniques, as well as evaluating their effectiveness in diverse populations with higher sensitivity and specificity for the detection of uterine fibroids, as well as biomarkers to aid in diagnosis.

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基于深度学习的超声图像子宫肌瘤检测
子宫肌瘤是源自子宫平滑肌层的常见良性肿瘤,通常会导致盆腔疼痛和生殖问题等症状。早期发现对于预防不孕症等并发症或需要进行子宫切除术等侵入性治疗至关重要。诊断子宫肌瘤的主要挑战之一是缺乏特异性症状,这可能与其他妇科疾病相似。这往往会导致诊断不足或误诊,从而延误适当的治疗。本研究提出了一种基于注意力的微调 EfficientNetB0 模型,用于对超声图像中的子宫肌瘤进行分类。注意力机制允许模型将注意力集中在图像的特定部分,并通过使其能够特别关注必要的亮点而忽略不相关的亮点来推进模型的执行。所提出的方法共使用了 1990 幅图像,分为两类:非子宫肌瘤和子宫肌瘤。数据增强方法通过将其与训练数据中更广泛的品种联系起来,提高了通用性和强度。所提模型的准确率达到了 0.99。未来的研究应侧重于提高诊断技术的准确性和效率,并评估其在不同人群中的有效性,提高检测子宫肌瘤的灵敏度和特异性,以及辅助诊断的生物标志物。
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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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