Mammography-based Computer-Aided Diagnostics for the Identification of Breast Cancer Based on Machine Learning

Abstract

Breast cancer is the most typical cancer overall and the one that affects women the most frequently. In 2022, there were 2.26 million or more brand-new cases of breast cancer in women. To identify the breast cancer in early stage, it's easy to cure. The apparent structure is examined using computer-aided diagnostic (CAD) technologies. The majority of existing diagnostic approaches rely on mammography mass features; however, the suggested strategy relies on MicroCalcification (MC) characteristics. In this research cancer cells are detected utilizing mammography images, pre-processing methods, segmentation, multi-layer perceptron with artificial algae algorithm and receiver operating characteristics (ROC) curve analysis. The national cancer institute (NCI) of provides mammogram scans. Normalization and median filtering were used as part of the pre-processing procedure. Segmentation is used to detect the location of MicroCalcification present cells using the local threshold approach. The MicroCalcification present cells and MicroCalcification missing cells are classified using the multi-layer perceptron (MPL) with artificial algae algorithm technique and also used to divide MicroCalcification-affected cells into the following categories: initial, very small, small, medium, high, and very high. ROC curve analysis was used to assess system performance. According to experimental findings, for the NCI, University of California Irvine (UCI), nathan kline institute (NKI), investigation of serial studies to predict your 1 (ISPY1), ISPY2, ISPY3, and ISPY4 datasets, the classification using the multi-layer perceptron with artificial algae method has the best accuracy of 96 percent when compared to random forest (RF), MPL with genetic algorithm (GA), hierarchical clustering random forest (HCRF), and convolutional neural network (CNN).