Background:
The breast cancer is not such a dreadful if the detection is not performed at an early. The chances
of having breast cancer is the married woman highly after the breast-feeding phase because, the cancer is formed from the
blocked milk ducts.
Introduction:
Recent days, the cancer is the major issue for human death. The women are mostly affected by breast cancer. This leads to deadliest life of most of the women. The breast cancer is caused while breast-feeding phase. The early
detection technique uses the mammography image analysis. Various researchers are used the artificial intelligence based
mammogram techniques. This process of mammography will reduce the death rate of the patients affected breast cancer.
This process is improved by image analysing, detection, screening, diagnosing, and other performance measures.
Methods:
The radial basis neural network will be used for the classification purpose. 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 training process. The cuckoo search algorithm will be used for this purpose.
Results:
Thus, the proposed optimum RBNN is determined to classify the breast cancer images. In this, the three set 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 literatures.
Conclusion:
Thus, it concluded with the efficient classification process of RBNN using cuckoo search algorithm for
breast cancer images. The mammogram images are taken into the recent research because the breast cancer is the major
issue for women. This process is carried to classify the various features for three set of properties. The optimized classifier improves the performance and provides the better result. In this proposed research work, the input image is filtered
using wiener filter and the classifier extracts the feature based on the breast image.
Evaluation of Fast Evolutionary Programming, Firefly Algorithm and Mutate-Cuckoo Search Algorithm In Single-Objective Optimization
