The Combination of Feature Extraction and Classification by Bag of Visual Words to Detect Breast Cancer for Improved Accuracy

Abstract

The main objective is to improve diagnostic accuracy of the breast cancer. The goal is to improve the accuracy of breast cancer detection by leveraging both the discriminative power of feature extraction and the robustness of classification using BoVW. By combining these techniques, the system aims to effectively distinguish between cancerous and non-cancerous tissues in medical images, thereby aiding in early diagnosis and treatment planning for breast cancer patient. The breast cancer sample images were gathered from the city's hospitals Chennai 2023 as well as the mini-MIAS database and DDSM database. The BoVW classifier is used to identify and classify breast tumor images according to their size. This improves the efficacy of the classification method developed for the CAD system's mammography breast cancer picture categorization. Several Computer Aided Diagnosis (CAD) systems have been created to use mammography images to identify breast cancer in its early stages. ANN attained 93.6%,94.18%, 93.2% and SVM gained 92%,92.44%,93.02% for Precision, Recall and Accuracy Respectively.The proposed method display excellent accuracy of 98.83% over the other methods. The novelty could stem from the seamless integration of multiple modalities within the feature extraction and classification framework.