Classification with 2-D Convolutional Neural Networks for breast cancer diagnosis
This addresses the challenge of using CNNs for breast cancer diagnosis from clinical data, which is incremental as it adapts existing CNN architectures to a new data type.
The authors tackled the problem of applying 2-D convolutional neural networks (CNNs) to non-image, non-time series clinical data by proposing novel preprocessing methods to transform 1-D vectors into 2-D graphical images with appropriate correlations. They tested on breast cancer datasets, achieving competitive results on WBC and outperforming other methods on WDBC.
Breast cancer is the most common cancer in women. Classification of cancer/non-cancer patients with clinical records requires high sensitivity and specificity for an acceptable diagnosis test. The state-of-the-art classification model - Convolutional Neural Network (CNN), however, cannot be used with clinical data that are represented in 1-D format. CNN has been designed to work on a set of 2-D matrices whose elements show some correlation with neighboring elements such as in image data. Conversely, the data examples represented as a set of 1-D vectors -- apart from the time series data -- cannot be used with CNN, but with other classification models such as Artificial Neural Networks or RandomForest. We have proposed some novel preprocessing methods of data wrangling that transform a 1-D data vector, to a 2-D graphical image with appropriate correlations among the fields to be processed on CNN. We tested our methods on Wisconsin Original Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) datasets. To our knowledge, this work is novel on non-image to image data transformation for the non-time series data. The transformed data processed with CNN using VGGnet-16 shows competitive results for the WBC dataset and outperforms other known methods for the WDBC dataset.