Background: 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.
Introduction: 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.
Methods: 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.
Results: 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.
Conclusion: 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.