Stacking approach for accurate Invasive Ductal Carcinoma classification

The accurate diagnosis of Breast cancer (BC) requires adequately exploiting Artificial intelligence (AI)-based methods in the diagnosing process. To tackle the issue of accurate BC diagnosis, we have proposed a deep learning-based stacking method (StackBC). In particular, we have incorporated deep learning models including Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) for the classification of Invasive Ductal Carcinoma (IDC). Additionally, Transfer Learning (TL) and Data Augmentation (DA) approaches have been incorporated to balance the dataset and adequately train the model. To further improve the predictive outputs of the model, we used the stacking technique. Among the three individual base classifiers, the performance of the GRU model was better. Hence, we selected the GRU as a meta classifier to distinguish between Non-IDC and IDC breast images. The experimental results confirmed that the StackBC method outperformed state-of-the-art methods.

Automated summary

Accurate diagnosis of breast cancer requires leveraging artificial intelligence-based methods. Researchers propose a deep learning-based stacking method (StackBC) that combines different deep learning models, transfer learning, and data augmentation to balance the dataset and train the model. The use of stacking technique further improves predictive outputs, outperforming state-of-the-art methods in breast cancer diagnosis.