Breast Cancer Classification using Deep Learned Features Boosted with Handcrafted Features

Breast cancer is one of the leading causes of death among women across the globe. It is difficult to treat if detected at advanced stages, however, early detection can significantly increase chances of survival and improves lives of millions of women. Given the widespread prevalence of breast cancer...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2023-01
Main Authors: Sajid, Unaiza, Rizwan Ahmed Khan, Shahid Munir Shah, Sheeraz Arif
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
Breast cancer
Classification
Datasets
Feature extraction
Histograms
Mammography
Medical research
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Breast cancer is one of the leading causes of death among women across the globe. It is difficult to treat if detected at advanced stages, however, early detection can significantly increase chances of survival and improves lives of millions of women. Given the widespread prevalence of breast cancer, it is of utmost importance for the research community to come up with the framework for early detection, classification and diagnosis. Artificial intelligence research community in coordination with medical practitioners are developing such frameworks to automate the task of detection. With the surge in research activities coupled with availability of large datasets and enhanced computational powers, it expected that AI framework results will help even more clinicians in making correct predictions. In this article, a novel framework for classification of breast cancer using mammograms is proposed. The proposed framework combines robust features extracted from novel Convolutional Neural Network (CNN) features with handcrafted features including HOG (Histogram of Oriented Gradients) and LBP (Local Binary Pattern). The obtained results on CBIS-DDSM dataset exceed state of the art.
ISSN:2331-8422
DOI:10.48550/arxiv.2206.12815