SMART HYBRID MODELS FOR IMPROVED BREAST CANCER DETECTION

Abstract

Breast cancer (BC) ranks the second most prevalent cancer among women globally and is the leading cause of female mortality. The conventional method for BC detection primarily relies on biopsy; this might be time-consuming and error prone. The substantial lives lost due to BC underscores its significant threat. Mitigating this threat focuses on early detection and prevention by adopting novel techniques. Many researchers have turned to Machine Learning algorithms to develop prognosis systems. We employ a combination of deep learning (DL) and machine learning (ML) algorithms for BC identification. Our approach is a hybrid Convolutional Neural Network (CNN) model, which performs better than other experimental and existing models. This model effectively categorizes histopathological images into either benign or malignant classes. We explored various methodologies, including CNN, CNN in conjunction with Support Vector Machine (SVM), CNN with Random Forest, and VGG-16 combined with XGBOOST. This research seeks to enhance the accuracy and efficiency of BC diagnosis. It contributes to more effective early detection and improved patient outcomes.

Keywords

• breast cancer
• deep learning
• cnn
• svm
• random forest
• vgg-16
• xgboost