Optimizing Breast Cancer Mammogram Classification Through a Dual Approach: A Deep Learning Framework Combining ResNet50, SMOTE, and Fully Connected Layers for Balanced and Imbalanced Data

Optimizing Breast Cancer Mammogram Classification Through a Dual Approach: A Deep Learning Framework Combining ResNet50, SMOTE, and Fully Connected Layers for Balanced and Imbalanced Data

Breast cancer is a global health concern where early and accurate diagnosis is crucial. Mammogram scans provide detailed imaging but require expert interpretation, which is time-consuming. While deep learning shows promise in medical image analysis, the prevalence of imbalanced datasets in medical d...

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Bibliographic Details
Main Authors: Abdullah Fahad A. Alshamrani, Faisal Saleh Zuhair Alshomrani
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Breast cancer
BI-RADs
classification
deep learning
features extractions
imbalance data
Online Access:https://ieeexplore.ieee.org/document/10819401/
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