Authors : Pallavi Kapse, Rimzim Janwe , Dr. Yogesh Golhar
DOI : 10.46335/IJIES.2024.9.9.9
Abstract – Breast Cancer is one of the highly increasing cancer diseases in women worldwide. Ensuring a precise diagnosis of this critical illness is pivotal for the patients’ survival. To attain outstanding results in breast cancer classification, we propose employing a sophisticated deep ensemble learning algorithm. Ensemble learning methods combine multiple individual CNN models to obtain better generalization performance. These models club the advantages of both deep learning models and ensemble learning to yield better classification accuracy. For the experimentation, the breast cancer histopathology dataset is used for training and validation of the proposed model from the online platform Kaggle. The accuracy obtained with the proposed model is about 94% which is outstanding in the given domain which shows the effectiveness of our model.
