Volume: 1 Issue: 2
Year: 2024, Page: 26-31, Doi: https://doi.org/10.54839/ijeaca.v1i2.1
Received: June 11, 2024 Accepted: Oct. 20, 2024 Published: Dec. 4, 2024
Breast cancer continues to pose a major global health challenge, underscoring the need for advanced detection and classification methods for mammograms. This study examines the effectiveness of ResNet-50 and VGG16 models in detecting and classifying multiview mammograms. Early and accurate detection of breast cancer is essential for improving patient outcomes and reducing mortality rates. Our approach began with the preparation of mammography images using various image processing techniques, including transfer learning and median filtering. The processed images were then used to train ResNet-50 and VGG16 models for detection and classification tasks. Our experiments demonstrated impressive performance, achieving an accuracy of 96% and an F1 score of 94.66% on the Digital Database for Screening Mammography (DDSM) datasets. These results underscore the potential of deep learning models, particularly ResNet-50, in effectively detecting and classifying multiview mammograms.
Keywords: Deep Learning for Improved Breast Cancer Detection: ResNet-50 vs VGG16
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© 2024 Patil & Dixit. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Patil RA, Dixit VV. (2024). Deep Learning for Improved Breast Cancer Detection: ResNet-50 vs VGG16. International Journal of Electronics and Computer Applications. 1(2): 26-31. https://doi.org/10.54839/ijeaca.v1i2.1