Early Detection of Breast Cancer Using Pretrained AlexNet Convolutional Neural Network
Brenda Guanulema, Daniela Osorio-Ordóñez, Helen Vaca-Farinango, Ariana Mieles-Salazar, Fernando Villalba-Meneses, Gabriela Cevallos-Bermeo, Carolina Cadena-Morejón, Diego A. Almeida-Galárraga, Andrés Tirado-Espín · 2023
Early detection of breast cancer is crucial in reducing mortality rates among women. Mammography imaging is an effective diagnostic technique, but it can be difficult to distinguish between healthy and cancerous tissue. Deep learning and convolutional neural networks (CNNs) have proven to be valuable tools in detecting breast cancer. In this study, we propose using a pre-trained CNN AlexNet to classify patients with breast cancer from healthy patients using digital imaging processing. The model was trained on a dataset of 1500 mammograms of healthy breasts and 1500 mammograms of breasts with malignant tumors and 720 images to validation. Additionally, data augmentation was performed to double the size of the training dataset to 6000. The proposed model showed a test accuracy of 98.9%, which is a higher accuracy value than the current state-of-the-art. This outcome underscores the potential of Alexnet as an effective tool for the early breast cancer detection through mammography images. This study highlights the potential of deep learning and CNNs in early breast cancer detection, therefore the exciting possibility for their real-world application can significantly improve the prognosis and quality of life for patients.