A Predictive Deep Learning Ensemble-Based Approach for Advanced Cancer Classification

Breast cancer is a significant contributor to the death rate of women in developing and underdeveloped nations. Timely identification and categorization of breast cancer can facilitate the administration of the most optimal therapy to patients. Using ensemble learning, we presented a novel deep-learning architecture for breast cancer detection and classification in breast ultrasound images. In the proposed work, image features are extracted using three pre-trained CNN architectures, DenseNet121, DenseNet169, and DenseNet201, which are then averaged to form an ensemble model. Experiments are conducted using Kaggle’s publicly available data set to evaluate the performance of the proposed architecture. Regarding accuracy in detecting and classifying breast cancer in ultrasound images, it has been visible that the proposed ensemble architecture outperforms other pre-defined deep learning architectures with an accuracy of 99.62%.