Paper Title
Breast Cancer Classification and Segmentation using Boosting Algorithms and U-Net

Abstract
The most common disease among women globally is breast cancer, which is treatable in 70-80 percent of individuals with early-stage cases. Various machine learning approaches are used for finding the cancer. But compared to machine learning techniques, the ensemble classifier provides a good results. Because to its benefits in modelling a vital feature detection from complicated BC datasets, machine learning (ML) is frequently utilised in the categorization of breast cancer (BC) patterns. In this research, we propose an ensemble of classifiers-based system for the automatic detection of breast cancer. We begin by reviewing various machine learning (ML) algorithms and ML algorithm ensembles. For the purpose of automatic Breast cancer detection, we offer an overview of Machine learning techniques, and ensemble of several classifiers. Additionally, we present and contrast multiple ensemble and other tested ML-based model variations. When noise, contrast adjustment, and distortions are present, segmentation becomes tedious. Before to segmentation, preprocessing is done to improve contrast and remove extraneous data from the image. The U-Net model will enhance computing when there are powerful GPUs present, facilitating the training of networks with additional layers. However, recent studies have shown that early use of preprocessing techniques would undoubtedly increase accuracy. Keywords - Breast Cancer, Machine Learning, Ensemble Learning, Segmentation, U-Net, mask

Author - T. Anil Raju, S. Gayathri, V. Jayanth, T.Pawan Kalyan

Published : Volume-10,Issue-5  ( May, 2023 )

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