# Breast Cancer Classification using Deep Learned Features Boosted with Handcrafted Features

arXiv: https://arxiv.org/abs/2206.12815 · HTML: https://mlsanity.com/paper/2206.12815/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 99 references that are in the map.
- First posted on arXiv 26 Jun 2022.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design](https://mlsanity.com/paper/1711.05775/) (2017)
- [Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)](https://mlsanity.com/paper/2001.00139/) (2020)
- [Machine learning and deep learning](https://mlsanity.com/paper/2104.05314/) (2021)
- [Automated Classification of Histopathology Images Using Transfer Learning](https://mlsanity.com/paper/1903.10035/) (2019)
- [Secondary Use of Electronic Health Record: Opportunities and Challenges](https://mlsanity.com/paper/2001.09479/) (2020)
- [Breast Tumor Segmentation and Shape Classification in Mammograms using Generative Adversarial and Convolutional Neural Network](https://mlsanity.com/paper/1809.01687/) (2018)
- [On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset](https://mlsanity.com/paper/1711.07831/) (2017)
- [A novel database of Children's Spontaneous Facial Expressions (LIRIS-CSE)](https://mlsanity.com/paper/1812.01555/) (2018)
- [A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers](https://mlsanity.com/paper/1703.05364/) (2017)
- [Deep Learning Based Computer-Aided Systems for Breast Cancer Imaging : A Critical Review](https://mlsanity.com/paper/2010.00961/) (2020)
- [Adversarial Deep Structured Nets for Mass Segmentation from Mammograms](https://mlsanity.com/paper/1710.09288/) (2017)
- [Can Autism be Catered with Artificial Intelligence-Assisted Intervention Technology? A Literature Review](https://mlsanity.com/paper/1803.05181/) (2018)
- [An Extensive Review on Spectral Imaging in Biometric Systems: Challenges and Advancements](https://mlsanity.com/paper/1807.05771/) (2018)
- [Artificial Intelligence For Breast Cancer Detection: Trends & Directions](https://mlsanity.com/paper/2110.00942/) (2021)

## Built on by (most cited first, 5 of 5)

- [Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks](https://mlsanity.com/paper/2410.02331/) (2024)
- [Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond](https://mlsanity.com/paper/2309.00064/) (2023)
- [Advancing Depression Detection on Social Media Platforms Through Fine-Tuned Large Language Models](https://mlsanity.com/paper/2409.14794/) (2024)
- [Pneumonia Detection in Chest X-Ray Images : Handling Class Imbalance](https://mlsanity.com/paper/2301.08479/) (2023)
- [Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration of Convolutional Neural Networks and Explainable AI](https://mlsanity.com/paper/2404.03892/) (2024)

## Abstract (arXiv)

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.