Department of Electronics and communication Engineering, Sree Vidyanikethan Engineering College, Tirupati, Andra Pradesh 517102, India.
Department of Computer Science and Engineering, Agni College of Technology, Chennai, 600130 Tamil Nadu, India.
Biomed Res Int. 2022 Aug 10;2022:8616535. doi: 10.1155/2022/8616535. eCollection 2022.
The second largest cause of mortality worldwide is breast cancer, and it mostly occurs in women. Early diagnosis has improved further treatments and reduced the level of mortality. A unique deep learning algorithm is presented for predicting breast cancer in its early stages. This method utilizes numerous layers to retrieve significantly greater amounts of information from the source inputs. It could perform automatic quantitative evaluation of complicated image properties in the medical field and give greater precision and reliability during the diagnosis. The dataset of axillary lymph nodes from the breast cancer patients was collected from Erasmus Medical Center. A total of 1050 images were studied from the 850 patients during the years 2018 to 2021. For the independent test, data samples were collected for 100 images from 95 patients at national cancer institute. The existence of axillary lymph nodes was confirmed by pathologic examination. The feed forward, radial basis function, and Kohonen self-organizing are the artificial neural networks (ANNs) which are used to train 84% of the Erasmus Medical Center dataset and test the remaining 16% of the independent dataset. 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.
全球第二大死因是乳腺癌，主要发生在女性身上。早期诊断进一步改善了治疗方法，降低了死亡率。本文提出了一种用于预测乳腺癌早期的独特深度学习算法。该方法利用多个层次从源输入中检索更多的信息。它可以对医学领域中复杂的图像属性进行自动定量评估，并在诊断过程中提供更高的精度和可靠性。从乳腺癌患者的腋窝淋巴结中收集数据集来自伊拉斯谟医疗中心。在 2018 年至 2021 年期间，从 850 名患者中研究了总共 1050 张图像。对于独立测试，从国家癌症研究所的 95 名患者中收集了 100 张图像的数据样本。腋窝淋巴结的存在通过病理检查得到证实。前馈、径向基函数和 Kohonen 自组织是人工神经网络 (ANN)，用于训练 84%的伊拉斯谟医疗中心数据集，并测试剩余的 16%独立数据集。根据准确性 (Ac)、敏感性 (Sn)、特异性 (Sf) 和接收器工作曲线 (Roc) 的结果来确定提出的模型性能，并与其他四位放射科医生的机制进行比较。研究结果表明，所提出的机制达到 95%的敏感性、96%的特异性和 98%的准确性，高于放射科医生的模型（90%的敏感性、92%的特异性和 94%的准确性）。深度学习算法可以通过利用初始乳腺癌患者的图像准确预测腋窝淋巴结转移的临床阴性结果。该方法为腋窝淋巴结阴性变化的患者提供了一种更早的腋窝淋巴结转移诊断技术。
