LAUSR

Breast cancer is one of the most precarious cancers that claims many women's' lives every year. The existing automated systems for mammography datasets are designed to detect the abnormalities and classify them as benign or malignant. However, these systems ignore the incompleteness of data due to missing or irrelevant values in the dataset, which severely impede the automated system's reliability. To enhance the reliability of the decision outcomes, this study proposes a composite imputed multilayer fully connected deep networks (MFCN) that can replace the incomplete values in the dataset to augment the efficient feature learning for breast cancer classification. The data imputation techniques and one‐hot encoding transformation applied to the dataset are proved to improve the feature representation. The imputed features extracted from the instances are fed into the MFCN to classify malignant and benign by utilizing mammographic mass (MM) and INbreast datasets. The performance of the proposed missing data imputation treated MFCN (MdI‐MFCN) is compared with competitive machine learning (ML) classifiers by incorporating stratified 6‐fold cross‐validation. The experimental results of the models are evaluated with and without imputed data using the five performance metrics, namely, accuracy, precision, recall, F1‐score, and specificity. The performance of the proposed MdI‐MFCN has outperformed the competitive classifiers with the highest accuracy of 93% and 98% of the area under the receiver operating characteristic (ROC) curve on MM and INbreast datasets, respectively. The simplicity and reliability of this proposed approach its potential in characterizing mammography datasets during breast cancer diagnosis.