Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
AI-Powered Breast Cancer Detection

AI-Powered Breast Cancer Detection

Kaliyaperumal, Saravanan
Ayyaswamy, Kathirvel

Paperback

General Computers

ISBN10: 6208457157
ISBN13: 9786208457150
Publisher: LAP Lambert Academic Publishing
Published: Oct 27 2025
Pages: 52
Weight: 0.18
Height: 0.12 Width: 6.00 Depth: 9.00
Language: English
Breast cancer remains one of the most prevalent and life-threatening diseases affecting women worldwide. Early and accurate detection plays a crucial role in improving survival rates and guiding effective treatment strategies. With the rapid advancements in Artificial Intelligence (AI), machine learning and computer vision techniques are increasingly being applied to automate the processes of breast cancer classification and image segmentation. This study focuses on the development of an intelligent framework that integrates recursive feature elimination (RFE) with a Support Vector Machine (SVM) classifier to enhance the accuracy and reliability of breast cancer detection and analysis. Experimental results demonstrate that the combination of segmentation techniques, RFE-based feature optimization, and SVM classification significantly improves diagnostic performance when compared to conventional machine learning approaches. The model achieves high accuracy, precision, and recall, making it suitable for clinical applications where reliability is critical.

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