Implementation of SVM Classifier To Perform Classification on the dataset of Breast Cancer Wisconin; to predict if the tumor is cancer or not.

Breast cancer (BC) is a prevalent disease and major cause of mortality among women worldwide. A substantial number of BC patients experience metastasis which in turn leads to treatment failure and death. The survival rate has been significantly increased thanks to the state of the art technologies and detection tools. In this study, we cross-compared the application of advanced artificial intelligence algorithms such as Logistic Regression, K-Nearest Neighbors, Discrete Cosine Transform, Random Forest Classifier, Support Vector Machines, Multilayer Perceptron, and Ensemble to diagnose BC metastasis. We further combined MLP with genetic algorithm (GA) as a hybrid method of intelligent analysis. The core data we used for comparison belonged to the images of both benign and malignant tumors and were taken from Wisconsin Breast Cancer dataset from the UCI repository. Our findings indicate that our MLP-GA hybrid algorithm can speed up diagnosis with higher accuracy rate than the individual patterns of algorithm. Two methods of comparison (Cross-Validation and Holdout) were applied to this study which produced consistent results

Advanced CNN yolo model to detection and classify breast cancer detection in real time with advanced version 8 of yolo algorithm

• Designed and implemented a machine learning model for breast cancer detection using Convolutional Neural Networks (CNN). • The model effectively classifies benign and malignant tumours with high accuracy, leveraging advanced image processing and feature extraction techniques.

A machine learning system classifying breast tumors as malignant or benign using the Wisconsin dataset. Employs advanced preprocessing with Yeo-Johnson transformation and PCA for dimensionality reduction. Multiple ML models including SVM with RBF and Polynomial kernels were evaluated to achieve accurate early cancer detection.

Breast cancer is a major threat to women, making early, accurate detection vital. Traditional imaging like mammography can suffer from noise and low contrast. Using mammograms with advanced analysis helps classify tumors as benign or malignant. A CAD-based website can support radiologists with faster, more precise decisions and improve outcomes.