NEW APPROACH FOR BREAST CANCER DETECTION- BASED MACHINE LEARNING TECHNIQUE

Abstract

The leading cause of cancer-related mortality is breast cancer. Breast cancer detection at an early stage is crucial.  Data on breast cancer can be diagnosed using a number of different Machine learning approaches. Automated breast cancer diagnosis using a Machine Learning model is introduced in this research.  Features were selected using Convolutional Neural Networks (CNNs) as a classifier model, and noise was removed using Contrast Limited Adaptive Histogram Equalization (CLAHE).  On top of that, the research compares five algorithms: Random Forest, SVM, KNN, Naïve Bayes classifier, and Logistic Regression. An extensive dataset of 3002 combined images was used to test the system. The dataset included information from 1400 individuals who underwent digital mammography between 2007 and 2015. Accuracy and precision are the metrics by which the system's performance is evaluated.   Due to its low computing power requirements and excellent accuracy, our suggested model is shown to be quite efficient in the simulation results.