Abstract
Between 2015 and 2020, 7.8 million women were diagnosed with breast cancer. If the cancer is discovered early, it can be completely cured. Computer-aided detection and diagnosis systems are a helpful tool. We propose such a system: after pre-processing the mammography, the region of interest is identified using an unsupervised manner. Textural features are extracted from the Gray-Level Co-occurrence Matrix and used with the Linear Discriminant Analysis classifier, obtaining a diagnosis: benign or malignant. The proposed system is tested on the Mini-MIAS dataset, reaching an accuracy score of 95% and a precision and specificity of 100%.
Citare
@Inproceedings{Moroz-Dubenco2023LinearDA,
author = {Cristiana Moroz-Dubenco and A. Andreica},
booktitle = {International Conference on Knowledge-Based Intelligent Information & Engineering Systems},
title = {Linear Discriminant Analysis Tumour Classification for Unsupervised Segmented Mammographies},
year = {2023}
}