Radial Basis Function Networks and Contrast-Limited Adaptive Histogram Equalization Filter Based Early-Stage Breast Cancer Detection Techniques

Abstract

Breast cancer is one of the most common types of cancer that kills women. When cells become uncontrollably large, cancer develops. As a result, detecting and classifying breast cancer in its early stages is essential so that patients can take the appropriate precautions. On the other hand, mammography images have relatively low sensitivity and effectiveness in detecting breast cancer. Furthermore, MRI (Magnetic Resonance Imaging) has higher detection sensitivity for breast cancer than mammography. In this research, a novel Radial Basis Function Networks model (RBFN) with a Mayfly Optimization Algorithm (MAO) mechanism has Breast MRI scans aid in the early detection of breast cancer. Following the system's training on Magnetic Resonance Imaging (MRI) breast images, a unique Contrast-Limited Adaptive Histogram Equalization (CLAHE) filter is developed for pre-processing noisy MRI image material. Backgrounds were removed before recovering breast cancer photos with a Contrast Limited Histogram Equalization (CLAHE) filter. Furthermore, the new study effort's performance is compared to earlier studies and this model is simulated using Python. The proposed model, RBFN-MAO, also outperforms previous models in terms of performance and precision with an accuracy of 97.54%. In comparison, it is 85.28, 80.95, 76.94, 85.39 and 90.32% for Convolution Neural Networks You Only Look Once (CNN-YOLO), Residual Networks (ResNet50), Diffusion Convolution Neural Networks (DCNN), Support Vector Machine (SVM) and Convolution Neural Network (CNN) models, respectively.