Implementation and Classification of Breast Cancer Histopathological Image Processing using Support Vector Machine

Deep learning (ML) is commonly used in the categorization of breast cancer (BC) pattern due to its advantages in modelling important image features from complex BC information. In our proposed system for autonomous prediction and diagnosis of BC using an ensemble of classifiers, different image areas are defined by different tissue types that have incredibly variable looks. Tumor prognosis, image categorization, and performance improvisation make up the three main contributions. Our approach is evaluated on a publicly available dataset. The performance of the application is then evaluated using performance improvisation and predictions. Lastly, the procedure’s efficacy is evaluated using accuracy, sensitivities, and precision.