A deep ensemble network model for classifying and predicting breast cancer. (5th December 2022)

• Record Type: Journal Article
• Authors: Subramanian, Arul Antran Vijay; Venugopal, Jothi Prakash
• Is Part Of: Computational intelligence. Volume 39:Number 2(2023)
• Page Start: 258
• Page End: 282
• Publication Date: 2022-12-05
• Subjects: breast cancer -- convolutional neural network -- deep ensemble -- machine learning -- recurrent neural network; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12563
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Breast cancer is one of the leading causes of death among women worldwide. In most cases, the misinterpretation of medical diagnosis plays a vital role in increased fatality rates due to breast cancer. Breast cancer can be diagnosed by classifying tumors. There are two different types of tumors, such as malignant and benign tumors. Identifying the type of tumor is a tedious task, even for experts. Hence, an automated diagnosis is necessary. The role of machine learning in medical diagnosis is eminent as it provides more accurate results in classifying and predicting diseases. In this paper, we propose a deep ensemble network (DEN) method for classifying and predicting breast cancer. This method uses a stacked convolutional neural network, artificial neural network and recurrent neural network as the base classifiers in the ensemble. The random forest algorithm is used as the meta‐learner for providing the final prediction. Experimental results show that the proposed DEN technique outperforms all the existing approaches in terms of accuracy, sensitivity, specificity, F‐score and area under the curve (AUC) measures. The analysis of variance test proves that the proposed DEN model is statistically more significant than the other existing classification models; thus, the proposed approach may aid in the early detection and diagnosis of breast cancer in women, hence aiding in the development of early treatment techniques to increase survival rate.