- Breast cancer is a prevalent disease that primarily affects women globally, but it can also affect men. Early detection is crucial for better treatment outcomes and mammography is a common screening method. Recommendations for mammograms vary by age and country. Early breast-cancer screening is vital for timely interventions. This paper aims to introduce artificial-intelligence methods through deep-learning approaches utilizing pre-trained CNN-based models for the diagnosis of masses depicted in breast images. These masses may be either malignant or benign, necessitating distinct management strategies for each scenario. The experiments conducted on pre-trained models (AlexNet, InceptionV3 and ResNet18) are designed to underscore the significance of selecting the batch size and adaptive learning rate in influencing the results, ultimately facilitating a notable enhancement in classification rates. Pre-trained models applied to a merged dataset comprising three datasetsBreast cancer is a prevalent disease that primarily affects women globally, but it can also affect men. Early detection is crucial for better treatment outcomes and mammography is a common screening method. Recommendations for mammograms vary by age and country. Early breast-cancer screening is vital for timely interventions. This paper aims to introduce artificial-intelligence methods through deep-learning approaches utilizing pre-trained CNN-based models for the diagnosis of masses depicted in breast images. These masses may be either malignant or benign, necessitating distinct management strategies for each scenario. The experiments conducted on pre-trained models (AlexNet, InceptionV3 and ResNet18) are designed to underscore the significance of selecting the batch size and adaptive learning rate in influencing the results, ultimately facilitating a notable enhancement in classification rates. Pre-trained models applied to a merged dataset comprising three datasets (Inbreast+MIAS+DDSM) yielded an accuracy of 93.7% for InceptionV3 and 88.9% for AlexNet. However, the most favorable outcome was observed with ResNet18, achieving an accuracy of 95% (with precision, recall and F1-score of 94.90%, 94.91% and 94.91%, respectively).…
