TY -的A2 Teekaraman Yuvaraja盟——Ashokkumar联合国非盟-米拉,s . AU -阿南丹,p . AU -没吃,Mantripragada Yaswanth Bhanu盟——Kalaivani k . s . AU - Alahmadi Tahani Awad盟——Alharbi Sulaiman阿里AU - Raghavan, s . s . AU - Jayadhas s Arockia PY - 2022 DA - 2022/08/10 TI -深度学习机制预测腋窝淋巴结转移患者的原发性乳腺癌SP - 8616535六世- 2022 AB -全球死亡率的第二大原因是乳腺癌,它主要发生在女性。早期诊断进一步改进治疗和降低死亡率。一个独特的深度学习算法预测乳腺癌的早期阶段。这种方法利用无数层检索明显更大量的信息从源输入。它可以执行复杂的图像属性的自动定量评价医学领域在诊断并给予更高的精度和可靠性。数据集的乳腺癌患者腋窝淋巴结收集的伊拉斯谟医学中心。总共850名患者的1050张照片进行了研究在2018年至2021年期间。独立的测试,数据样本收集的95例患者的100张照片在国家癌症研究所。的存在证实了腋窝淋巴结病理检查。径向基函数,前馈和Kohonen自组织人工神经网络(ann)用于火车84%的伊拉斯谟医学中心的独立数据集的数据集和测试剩下的16%。 The proposed model performance was determined in terms of accuracy (Ac), sensitivity (Sn), specificity (Sf), and the outcome of the receiver operating curve (Roc), which was compared to the other four radiologists’ mechanism. The result of the study shows that the proposed mechanism achieves 95% sensitivity, 96% specificity, and 98% accuracy, which is higher than the radiologists’ models (90% sensitivity, 92% specificity, and 94% accuracy). Deep learning algorithms could accurately predict the clinical negativity of axillary lymph node metastases by utilizing images of initial breast cancer patients. This method provides an earlier diagnostic technique for axillary lymph node metastases in patients with medically negative changes in axillary lymph nodes. SN - 2314-6133 UR - https://doi.org/10.1155/2022/8616535 DO - 10.1155/2022/8616535 JF - BioMed Research International PB - Hindawi KW - ER -