Ensemble CNNs for Breast Tumor Classification
This work addresses breast cancer diagnosis for medical imaging, but it is incremental as it applies standard ensemble techniques to existing models.
The authors tackled breast tumor classification from mammographic images by developing an ensemble of three CNNs (XceptionNet, DenseNet, EfficientNet), achieving up to a 5% performance improvement with accuracy, precision, and recall of 88%, 85%, and 76% respectively on a public dataset.
To improve the recognition ability of computer-aided breast mass classification among mammographic images, in this work we explore the state-of-the-art classification networks to develop an ensemble mechanism. First, the regions of interest (ROIs) are obtained from the original dataset, and then three models, i.e., XceptionNet, DenseNet, and EfficientNet, are trained individually. After training, we ensemble the mechanism by summing the probabilities outputted from each network which enhances the performance up to 5%. The scheme has been validated on a public dataset and we achieved accuracy, precision, and recall 88%, 85%, and 76% respectively.