Abstract
Masses are one of the early signs of breast cancer, and the survival rate of women suffering from breast cancer can be improved if masses can be correctly identified as benign or malignant. However, their classification is challenging due to the similarity in texture patterns of both types of mass. The existing methods for this problem have low sensitivity and specificity. Based on the hypothesis that diverse contextual information of a mass region forms a strong indicator for discriminating benign and malignant masses and the idea of the ensemble classifier, we introduce a computer-aided system for this problem. The system uses multiple regions of interest (ROIs) encompassing a mass region for modeling diverse contextual information, a single ResNet-50 model (or its density-specific modification) as a backbone for local decisions, and stacking with SVM as a base model to predict the final decision. A data augmentation technique is introduced for fine-tuning the backbone model. The system was thoroughly evaluated on the benchmark CBIS-DDSM dataset using its provided data split protocol, and it achieved a sensitivity of 98.48% and a specificity of 92.31%. Furthermore, it was found that the system gives higher performance if it is trained and tested using the data from a specific breast density BI-RADS class. The system does not need to fine-tune/train multiple CNN models; it introduces diverse contextual information by multiple ROIs. The comparison shows that the method outperforms the state-of-the-art methods for classifying mass regions into benign and malignant. It will help radiologists reduce their burden and enhance their sensitivity in the prediction of malignant masses.
Highlights
Breast cancer is one of the deadliest and most common cancers among women
Based on the hypothesis that diverse contextual information helps better discriminate benign and malignant masses and the idea of an ensemble classifier, we introduce a method for automatically classifying mass regions into benign and malignant
An important decision is about extracting contextual information; we evaluated two methods and found that multi-context regions of interest (ROIs) represent the diverse contextual information in a better way and helps in better discrimination of benign and malignant mass regions
Summary
Breast cancer is one of the deadliest and most common cancers among women. According to a World Health Organization (WHO) report, breast cancer accounts for 2.26 million of all diagnosed cancers and 685,000 cancer-related deaths worldwide [1]. An ensemble classifier can be used for better performance because an ensemble classifier strategy achieves a more promising performance than using a single classifier Adopting this method for deep learning-based classifiers is costly in memory and computing complexity since training and storing many CNN models is costly. Based on the hypothesis that diverse contextual information helps better discriminate benign and malignant masses and the idea of an ensemble classifier, we introduce a method for automatically classifying mass regions into benign and malignant. It is computationally efficient, effective, and requires less memory.
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