Keywords
Deep learning
Mammography
Image classification
Computer-aided diagnosis (CAD)
How to Cite
Abstract
Breast cancer represents a significant global health challenge, ranking as one of the most prevalent malignancies among women. Early and accurate diagnosis through medical imaging is paramount for improving patient outcomes, with mammography serving as the gold standard for screening. However, the interpretation of mammograms can be challenging and subject to inter-observer variability. This study aims to comparatively evaluate the performance and computational efficiency of four prominent Convolutional Neural Network (CNN) architectures for the automated classification of breast cancer from mammogram images. Utilizing a publicly available dataset comprising 3,383 mammogram images classified as either Benign or Malignant, we trained and evaluated four distinct models: InceptionV3, DenseNet169, InceptionV4, and ResNet50. The results demonstrate that the DenseNet169 architecture achieved superior performance across all evaluated metrics, attaining the highest accuracy (73.33%), precision (70.45%), recall (67.83%), and F1-score (68.60%). Notably, DenseNet169 also exhibited the highest computational efficiency, featuring the lowest parameter count (12.49M) among the tested models. These findings suggest that DenseNet169 offers an optimal balance between diagnostic accuracy and model efficiency, positioning it as a highly promising candidate for integration into clinical decision support systems to aid radiologists in the early detection of breast cancer.
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