Breast Cancer Detection in Mammography Images Using Transfer Learning Model

Breast cancer remains a significant global health concern, emphasizing the need for advanced and accurate diagnostic tools. This research paper focuses on the application of a Transfer Learning model for the detection of breast cancer in mammography images. Leveraging the power of deep learning, Transfer Learning enables the utilization of pre-trained models on large datasets, optimizing performance even with limited data availability. The study employs a diverse dataset comprising mammography images from various sources, ensuring a comprehensive representation of breast cancer cases. A Convolutional Neural Network (CNN) architecture, pre-trained on a vast dataset, is fine-tuned using the mammography dataset to harness its feature extraction capabilities for breast cancer detection. This approach allows the model to learn intricate patterns and abnormalities indicative of malignancies. Key steps involve the pre-processing of mammography images to enhance the quality and extraction of relevant features through the Transfer Learning model. The research investigates the model's efficacy in distinguishing between benign and malignant cases, evaluating its accuracy, sensitivity, specificity, and precision. The research proposed EfficientNet B3 with a DenseNet transfer learning model for classifying mammography images into benign or malignant categories. Also, the impact of varying architectures and hyperparameters on the model's performance is explored for optimization. Results demonstrate promising outcomes, with the Transfer Learning model exhibiting a high degree of accuracy in breast cancer detection. The model's ability to generalize across diverse datasets underscores its robustness and potential for real-world clinical applications. Visions gained from this research contribute to the ongoing discourse on the integration of advanced technologies in breast cancer diagnostics. This research signifies a crucial step towards enhancing the accuracy and efficiency of breast cancer detection, emphasizing the potential of Transfer Learning in revolutionizing mammography-based diagnostic approaches. Findings show that the EfficientNet-B3 and DenseNet have loss values ranging between 0.9, while VGG16's loss values are significantly higher, ranging from 7. The findings hold implications for improving early detection, facilitating timely interventions, and ultimately advancing outcomes for individuals at risk of breast cancer.