Breast Cancer Detection And Localization Using Region-Based Convolutional Neural Networks (RCNN): A Deep Learning Approach

Authors

  • Sandhya C, Anoop B K

Keywords:

RCNN, Breast Cancer, Deep Learning, Mammography, Tumor Detection, Medical Imaging.

Abstract

Breast cancer remains a major global health challenge, where timely and accurate detection is crucial for effective treatment. Conventional interpretation of ultrasound or MRI scans often suffers from diagnostic variability and human error. This study presents a Region-based Convolutional Neural Network (RCNN) model for the automated detection and localization of breast tumors using ultrasound and MRI imaging data. The model is trained and validated on benchmark medical datasets and evaluated through key metrics including accuracy, precision, recall, and Intersection over Union (IoU). Results indicate that the proposed RCNN framework significantly improves tumor detection accuracy and spatial localization effectiveness.

DOI: https://doi.org/10.17762/ijisae.v12i8s.7645

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References

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Published

13.12.2023

How to Cite

Sandhya C. (2023). Breast Cancer Detection And Localization Using Region-Based Convolutional Neural Networks (RCNN): A Deep Learning Approach. International Journal of Intelligent Systems and Applications in Engineering, 12(8s), 702–710. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7645

Issue

Vol. 12 No. 8s (2024)

Section

Research Article

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