Department of Obstetrics and Gynecology, The Second Hospital of Shanxi Medical University, No. 382, Wuyi Road, Xinghualing District, Taiyuan City, 030001, Shanxi Province, China.
BMC Med Imaging. 2024 Aug 19;24(1):218. doi: 10.1186/s12880-024-01389-z.
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.
子宫肌瘤是一种常见的良性肿瘤，起源于子宫的平滑肌层，常导致盆腔疼痛和生殖问题等症状。早期发现对于预防不孕或需要进行侵入性治疗（如子宫切除术）等并发症至关重要。诊断子宫肌瘤的主要挑战之一是缺乏特异性症状，这些症状可能与其他妇科疾病相似。这往往导致诊断不足或误诊，延误了适当的治疗。在这项研究中，提出了一种基于注意力的微调 EfficientNetB0 模型，用于从超声图像中分类子宫肌瘤。注意力机制允许模型专注于图像的特定部分，并通过赋予其专门关注重要特征而忽略不相关特征来推动模型的执行。该方法总共使用了 1990 张图像，分为两类：非子宫肌瘤和子宫肌瘤。通过数据增强方法连接起来，通过在训练数据中暴露于更广泛的变化范围来提高泛化和强度。所提出的模型获得了 0.99 的准确率。未来的研究应集中于提高诊断技术的准确性和效率，并评估其在具有更高敏感性和特异性的不同人群中检测子宫肌瘤以及生物标志物辅助诊断的有效性。
