# Classification with 2-D Convolutional Neural Networks for breast cancer diagnosis

arXiv: https://arxiv.org/abs/2007.03218 · HTML: https://mlsanity.com/paper/2007.03218/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2020.

## Built on by (most cited first, 8 of 8)

- [LM-IGTD: a 2D image generator for low-dimensional and mixed-type tabular data to leverage the potential of convolutional neural networks](https://mlsanity.com/paper/2406.14566/) (2024)
- [miMamba: EEG-based Emotion Recognition with Multi-scale Inverted Mamba Models](https://mlsanity.com/paper/2409.07589/) (2024)
- [TabSOM: A tabular-to-image encoding method based on self-organizing maps](https://mlsanity.com/paper/2608.13513/) (2026)
- [Fusing Medical Image Features and Clinical Features with Deep Learning for Computer-Aided Diagnosis](https://mlsanity.com/paper/2103.05855/) (2021)
- [Deep learning for classification of noisy QR codes](https://mlsanity.com/paper/2307.10677/) (2023)
- [Breast Cancer classification by adaptive weighted average ensemble of previously trained models](https://mlsanity.com/paper/2311.13206/) (2023)
- [Test-Time Augmentation for Tabular-to-Image Classifiers under Distribution Shifts](https://mlsanity.com/paper/2608.03557/) (2026)
- [VG-TIE: An interpretable tabular-to-image encoding method based on visibility graphs](https://mlsanity.com/paper/2609.29650/) (2026)

## Abstract (arXiv)

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.