Application of Computer-Aided Techniques for Detecting and Assessing Breast Cancer in Mammograms

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Anurag Agarwal; Komal Kalyani; Sagar Kumar; Vishal Kumar

Application of Computer-Aided Techniques for Detecting and Assessing Breast Cancer in Mammograms

Authors: Anurag Agarwal, Komal Kalyani, Sagar Kumar, Vishal Kumar

Unique Paper ID: 170609
Volume: 11
Issue: 7
PageNo: 797-802

Keywords: Mammography; Breast Cancer; Convolutional Neural Networks; Classification; Detection; Segmentation.

Abstract:
Breast cancer is responsible for a significantly high annual mortality rate and remains the most prevalent cancer among women. It is also considered the second deadliest form of cancer. This underscores the significance of progress in early detection techniques for improving health outcomes. Crucial to this effort is the enhancement of treatment efficacy and patient survival through accurate cancer prognosis. Automated systems for disease identification provide dependable, effective, and quick responses from medical professionals, thereby decreasing the risk of fatalities. Recently, breast cancer screening techniques utilizing deep-learning have shown promise in early detection, thanks to artificial intelligence (AI). Unlike traditional machine learning, deep learning reduces manual intervention during feature extraction. This paper provides an overview of deep learning techniques, available data, and breast cancer screening methods including mammography, thermography, ultrasound, and MRI. This research seeks to forecast breast cancer using a combination of demographic, laboratory, and mammographic information through various deep-learning techniques. Furthermore, we investigate the utilization of artificial intelligence in breast cancer clinical trials and evaluate our proposed method against existing algorithms.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 7
PageNo: 797-802

Application of Computer-Aided Techniques for Detecting and Assessing Breast Cancer in Mammograms

Available:https://ijirt.org/article?manuscript=170609

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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