Detection and Prediction of Breast Cancer Using Improved Faster Regional Convolutional Neural Network Based on Multilayer Perceptron’s Network

Abstract

One of the most frequent causes of death for women worldwide is breast cancer. In most cases, breast cancer can be quickly identified if certain symptoms emerge. But many women with breast cancer don’t show any symptoms. So, it is very critical to detect this disease in early stage also numerous radiologists are needed to diagnose this disease which is quite expensive for the majority of cancer hospitals. To address these concerns, the proposed methodology creates a Faster-Regional Convolutional Neural Network (Faster-RCNN) for recognizing breast cancer. Ultrasound images are collected and pre-processed utilizing resizing, adaptive median filter, histogram global contrast enhancement and high boost filtering. Image resizing is utilized to change the image size without cutting anything out. Adaptive median filter is utilized to remove unwanted noise present in the resized image. Histogram global contrast enhancement is used to enhancing the contrast level of the image. High boost filtering is utilized to sharpening the edges present in the image. After that, pre-processed images are fetched as an input to Faster R-CNN, which extract the features and segment the accurate region of the tumour. These segmented regions are classified using Multilayer Perceptron’s for detecting whether the patients are affected by breast cancer or not. According to the experimental study, the proposed approach achieves 97.1% correctness, 0.03% error, 91% precision and 93% specificity. Therefore, the developed approach attains better performance compared to other existing approaches. This prediction model helps to detect breast cancer at early stage and improve patient’s living standard.