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Related Experiment Video

Updated: Jul 13, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Breast Cancer Detection and Prevention Using Machine Learning.

Arslan Khalid¹, Arif Mehmood¹, Amerah Alabrah²

¹Faculty of Computing, Islamia University of Bahawalpur, Bahawalpur 63100, Punjab, Pakistan.

Diagnostics (Basel, Switzerland)

|October 14, 2023

Summary

This summary is machine-generated.

This study introduces an efficient deep learning model for accurate breast cancer detection in mammograms, requiring less computational power for early diagnosis and improved patient outcomes.

Keywords:

breast cancer healthcare machine learning

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Area of Science:

Medical Imaging
Artificial Intelligence in Oncology
Computational Biology

Background:

Breast cancer is a significant cause of mortality in women, particularly in developing nations.
Early detection and accurate classification of breast cancer subtypes, such as invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS), are critical for effective treatment.
Advancements in artificial intelligence (AI) and machine learning (ML), including convolutional neural networks (CNNs), show promise for improving breast cancer diagnosis.

Purpose of the Study:

To propose an efficient deep learning model for recognizing breast cancer in digital mammograms of varying densities.
To develop a computationally efficient model that overcomes the high resource demands of existing AI-based methods.
To enhance the accuracy and reliability of breast cancer detection and classification.

Main Methods:

A novel deep learning model was developed for breast cancer recognition.
Feature selection involved three modules: low-variance feature removal, univariate feature selection, and recursive feature elimination.
The model was trained and tested on a dataset of 3002 digital mammograms from 1501 individuals, incorporating craniocaudal and medial-lateral views.

Main Results:

The proposed deep learning model demonstrated high efficiency and accuracy in detecting breast cancer from mammograms.
The model requires significantly less computational power compared to existing methods.
Comparison with six classification models (RF, DT, KNN, LR, SVC, linear SVC) indicated superior performance of the proposed approach.

Conclusions:

The developed deep learning model offers an efficient and accurate solution for breast cancer detection using mammography.
This approach has the potential to improve early diagnosis, especially in resource-limited settings.
Further research can explore the integration of this model into clinical workflows for enhanced breast cancer screening and management.