论文标题
PCONET：从卵巢超声图像中检测多囊卵巢综合征（PCOS）的卷积神经网络结构
PCONet: A Convolutional Neural Network Architecture to Detect Polycystic Ovary Syndrome (PCOS) from Ovarian Ultrasound Images
论文作者
论文摘要
多囊卵巢综合征（PCOS）是生殖年龄妇女中普遍存在的终产功能障碍。 PCOS是女性中过量的雄激素（一组性激素）引起的综合征的组合。包括痤疮，脱发，多肌症，高狂力血症，寡卵形等综合症是由PCOS引起的。这也是女性不育的主要原因。估计有15％的生殖妇女受到全球PCOS的影响。由于其有害影响的严重程度，不可夸大其词，因此需要早日检测PCOS。在本文中，我们开发了PCONET - 一种卷积神经网络（CNN） - 从卵巢超声图像中检测多元卵巢。我们还通过利用转移学习方法对Polcystic卵巢超声图像进行分类，还通过使用45层的验证卷积神经网络进行了微调。我们已经在各种定量性能评估参数上比较了这两个模型，并证明PCONET是这两个模型的优势，其精度为98.12％，而微调InceptionV3在测试图像上的精度为96.56％。
Polycystic Ovary Syndrome (PCOS) is an endrocrinological dysfunction prevalent among women of reproductive age. PCOS is a combination of syndromes caused by an excess of androgens - a group of sex hormones - in women. Syndromes including acne, alopecia, hirsutism, hyperandrogenaemia, oligo-ovulation, etc. are caused by PCOS. It is also a major cause of female infertility. An estimated 15% of reproductive-aged women are affected by PCOS globally. The necessity of detecting PCOS early due to the severity of its deleterious effects cannot be overstated. In this paper, we have developed PCONet - a Convolutional Neural Network (CNN) - to detect polycistic ovary from ovarian ultrasound images. We have also fine tuned InceptionV3 - a pretrained convolutional neural network of 45 layers - by utilizing the transfer learning method to classify polcystic ovarian ultrasound images. We have compared these two models on various quantitative performance evaluation parameters and demonstrated that PCONet is the superior one among these two with an accuracy of 98.12%, whereas the fine tuned InceptionV3 showcased an accuracy of 96.56% on test images.
