Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India.
Curr Med Imaging. 2021;17(1):97-108. doi: 10.2174/1573405616666200516172118.
Breast cancer is a curable disease if diagnosed at an early stage. The chances of having breast cancer are the lowest in married women after the breast-feeding phase because the cancer is formed from the blocked milk ducts.
Nowadays, cancer is considered the leading cause of death globally. Breast cancer is the most common cancer among females. It is possible to develop breast cancer while breast-feeding a baby, but it is rare. Mammography is one of the most effective methods used in hospitals and clinics for early detection of breast cancer. Various researchers are used in artificial intelligence- based mammogram techniques. This process of mammography will reduce the death rate of the patients affected by breast cancer. This process is improved by the image analysing, detection, screening, diagnosing, and other performance measures.
The radial basis neural network will be used for classification purposes. The radial basis neural network is designed with the help of the optimization algorithm. The optimization is to tune the classifier to reduce the error rate with the minimum time for the training process. The cuckoo search algorithm will be used for this purpose.
Thus, the proposed optimum RBNN is determined to classify breast cancer images. In this, the three sets of properties were classified by performing the feature extraction and feature reduction. In this breast cancer MRI image, the normal, benign, and malignant is taken to perform the classification. The minimum fitness value is determined to evaluate the optimum value of possible locations. The radial basis function is evaluated with the cuckoo search algorithm to optimize the feature reduction process. The proposed methodology is compared with the traditional radial basis neural network using the evaluation parameter like accuracy, precision, recall and f1-score. The whole system model is done by using Matrix Laboratory (MATLAB) with the adaptation of 2018a since the proposed system is most efficient than most recent related literature.
Thus, it concluded with the efficient classification process of RBNN using a cuckoo search algorithm for breast cancer images. The mammogram images are taken into recent research because breast cancer is a major issue for women. This process is carried to classify the various features for three sets of properties. The optimized classifier improves performance and provides a better result. In this proposed research work, the input image is filtered using a wiener filter, and the classifier extracts the feature based on the breast image.
如果在早期诊断出乳腺癌，那么这种疾病是可以治愈的。在哺乳期过后，已婚女性患乳腺癌的几率最低，因为癌症是由堵塞的乳腺导管形成的。
如今，癌症被认为是全球范围内导致死亡的主要原因。乳腺癌是女性中最常见的癌症。在哺乳婴儿时有可能患上乳腺癌，但这种情况很少见。乳腺 X 线照相术是医院和诊所用于早期发现乳腺癌的最有效方法之一。各种研究人员都在使用基于人工智能的乳腺 X 线照相术技术。这种乳腺 X 线照相术的过程将降低受乳腺癌影响的患者的死亡率。通过图像分析、检测、筛查、诊断和其他性能指标来改进这一过程。
将使用径向基神经网络进行分类。径向基神经网络是在优化算法的帮助下设计的。优化的目的是调整分类器，以在训练过程中用最短的时间降低错误率。将使用布谷鸟搜索算法来实现这一目的。
因此，确定提出的最佳 RBNN 用于分类乳腺癌图像。在这三种情况下，通过执行特征提取和特征减少来分类三组属性。在这种乳腺癌 MRI 图像中，将正常、良性和恶性进行分类。确定最小适应度值，以评估可能位置的最佳值。使用布谷鸟搜索算法评估径向基函数，以优化特征减少过程。将所提出的方法与传统的径向基神经网络进行比较，使用准确性、精度、召回率和 F1 分数等评估参数。整个系统模型是通过使用 2018a 年的矩阵实验室（MATLAB）完成的，因为所提出的系统比大多数最近的相关文献更有效。
因此，它得出结论，使用布谷鸟搜索算法的 RBNN 可以高效地对乳腺癌图像进行分类。乳腺 X 线照相术图像被纳入了最近的研究中，因为乳腺癌是女性的一个主要问题。这个过程是为了对三组属性的各种特征进行分类。优化的分类器提高了性能，并提供了更好的结果。在这项提出的研究工作中，输入图像使用维纳滤波器进行滤波，然后分类器基于乳腺图像提取特征。
